SortaDate Analysis
David Cerny
3/16/2017
Individual UCEs
The gene trees were constructed using ETE toolkit:
Preparatory step: installing Miniconda, following the instructions from http://etetoolkit.org/download/
The gene trees were estimated using RAxML:
ete3 build -w standard_raxml -n /Users/David/Grive/Alfaro_Lab/Acanthomorpha/sate-gblocks-clean-min-114-taxa/FASTA/uce-3.fasta -o /Users/David/Grive/Alfaro_Lab/Acanthomorpha/sate-gblocks-clean-min-114-taxa/FASTA/uce-3-treeAutomatically generate the corresponding command for each of the 230 alignment files in the directory:
cd /Users/David/Grive/Alfaro_Lab/Acanthomorpha/sate-gblocks-clean-min-114-taxa/FASTA
lsCopy the output to a text file:
# Get the 1st part of the command
locus_table <- read.table("Set1-individual-loci.txt")
n <- length(unlist(locus_table))
inputfile <- vector()
for(i in locus_table) {
inputfile <- append(inputfile, paste("ete3 build -w standard_raxml -n /Users/David/Grive/Alfaro_Lab/Acanthomorpha/sate-gblocks-clean-min-114-taxa/FASTA/", i, sep = ""))
}
# Length check
length(inputfile) - n
# Remove file endings from the locus names
names_and_endings <- as.character(as.vector(as.matrix(locus_table)))
locus_names <- vector()
for(i in names_and_endings) {
locus_names <- append(locus_names, sapply(strsplit(i, split='.', fixed=TRUE), function(x) (x[1])))
}
# Get the 2nd part of the command
outputfile <- vector()
for(i in locus_names) {
outputfile <- append(outputfile, paste(" -o /Users/David/Grive/Alfaro_Lab/Acanthomorpha/sate-gblocks-clean-min-114-taxa/", i, sep=""))
}
# Length check
length(outputfile) - n
# Get the entire command
commands <- paste(inputfile, outputfile, "-tree &&", sep="")
# Length check
length(commands) - n
# Uncomment to print to file:
# write(commands, "gene-tree-analysis.sh")#!/bin/bash was then inserted into the first line of the file to convert it into a shell script. The script was run as follows:
chmod 755 /Users/David/Grive/Alfaro_Lab/SortaDate/Locus_analysis/gene-tree-analysis.sh
/Users/David/Grive/Alfaro_Lab/SortaDate/Locus_analysis/gene-tree-analysis.shWrite a new script that will copy the trees from the nested subdirectories to the same directory where the alignment files are located:
# 1st part of the command
copyfiles <- vector()
for(i in locus_names) {
copyfiles <- append(copyfiles, paste("cp /Users/David/Grive/Alfaro_Lab/Acanthomorpha/sate-gblocks-clean-min-114-taxa/", i, sep = ""))
}
# 2nd part of the command
subdirectories <- vector()
for(i in locus_names) {
subdirectories <- append(subdirectories, paste("-tree/clustalo_default-none-none-raxml_default/", i, sep = ""))
}
# Get the entire command
copying <- paste(copyfiles, subdirectories, ".fasta.final_tree.nw /Users/David/Grive/Alfaro_Lab/Acanthomorpha/sate-gblocks-clean-min-114-taxa/FASTA/ &&", sep="")
# Uncomment to print to file:
# write(copying, "copy-trees.sh")Write a third script to rename the tree files so that they have the .tre file ending, which SortaDate looks for while searching its target directory:
# 1st part of the command
oldname <- vector()
for(i in locus_names) {
oldname <- append(oldname, paste("mv /Users/David/Grive/Alfaro_Lab/Acanthomorpha/sate-gblocks-clean-min-114-taxa/FASTA/", i, sep = ""))
}
# 2nd part of the command
newname <- vector()
for(i in locus_names) {
newname <- append(newname, paste(".fasta.final_tree.nw /Users/David/Grive/Alfaro_Lab/Acanthomorpha/sate-gblocks-clean-min-114-taxa/FASTA/", i, sep = ""))
}
# Get the entire command:
renametrees <- paste(oldname, newname, ".tre &&", sep = "")
# Uncomment to print to file:
write(renametrees, "rename-trees.sh")Phase 1: get_var_length
python src/get_var_length.py /Users/David/Grive/Alfaro_Lab/Acanthomorpha/sate-gblocks-clean-min-114-taxa/FASTA/ --flend .tre --outf Locus_analysis/var-uces --outg alepisaurus_ferox,ceratoscopelus_warmingiiPhase 2: get_bp_genetrees
python src/get_bp_genetrees.py /Users/David/Grive/Alfaro_Lab/Acanthomorpha/sate-gblocks-clean-min-114-taxa/FASTA/ /Users/David/Grive/Alfaro_Lab/Acanthomorpha/12_no_outgroups_scheme_3.tre --flend .tre --outf Locus_analysis/bp-ucesPhase 3: combine_results
python src/combine_results.py Locus_analysis/var-uces Locus_analysis/bp-uces --outf Locus_analysis/comb-ucesPhase 4: get_good_genes
In order of descending priority: bipartition support, root-to-tip variance, tree length
python src/get_good_genes.py Locus_analysis/comb-uces --max 3 --order 3,1,2 --outf Locus_analysis/gg-uces-312name root-to-tip_var treelength bipartition
uce-1184.tre 0.00510364 5.59114 0.491379310345
uce-383.tre 0.0094896 4.68126 0.465517241379
uce-1317.tre 0.0200846 8.06488 0.465517241379In order of descending priority: bipartition support, tree length, root-to-tip variance
python src/get_good_genes.py Locus_analysis/comb-uces --max 3 --order 3,2,1 --outf Locus_analysis/gg-uces-321name root-to-tip_var treelength bipartition
uce-1184.tre 0.00510364 5.59114 0.491379310345
uce-383.tre 0.0094896 4.68126 0.465517241379
uce-1317.tre 0.0200846 8.06488 0.465517241379In order of descending priority: root-to-tip variance, tree length, bipartition support
python src/get_good_genes.py Locus_analysis/comb-uces --max 3 --order 1,2,3 --outf Locus_analysis/gg-uces-123name root-to-tip_var treelength bipartition
uce-1075.tre 7.59021e-05 0.363795 0.0689655172414
uce-446.tre 0.000230387 0.897702 0.26724137931
uce-855.tre 0.000309407 1.03296 0.301724137931In order of descending priority: root-to-tip variance, bipartition support, tree length
python src/get_good_genes.py Locus_analysis/comb-uces --max 3 --order 1,3,2 --outf Locus_analysis/gg-uces-132name root-to-tip_var treelength bipartition
uce-1075.tre 7.59021e-05 0.363795 0.0689655172414
uce-446.tre 0.000230387 0.897702 0.26724137931
uce-855.tre 0.000309407 1.03296 0.301724137931In order of descending priority: tree length, root-to-tip variance, bipartition support
python src/get_good_genes.py Locus_analysis/comb-uces --max 3 --order 2,1,3 --outf Locus_analysis/gg-uces-213name root-to-tip_var treelength bipartition
uce-1075.tre 7.59021e-05 0.363795 0.0689655172414
uce-737.tre 0.000743068 0.814482 0.137931034483
uce-446.tre 0.000230387 0.897702 0.26724137931In order of descending priority: tree length, bipartition support, root-to-tip variance
python src/get_good_genes.py Locus_analysis/comb-uces --max 3 --order 2,3,1 --outf Locus_analysis/gg-uces-231name root-to-tip_var treelength bipartition
uce-1075.tre 7.59021e-05 0.363795 0.0689655172414
uce-737.tre 0.000743068 0.814482 0.137931034483
uce-446.tre 0.000230387 0.897702 0.26724137931k-means partitions
Note that the largest, slowest-evolving partition (“ccf55a6ee6d62f840a124bcc0c98ecf5”; 132 kb) was excluded from the first round of analyses for computational reasons. Attempts to analyze it in RAxML after the remaining 31 analyses finished up were unsuccessful.
# Get the 1st part of the command
kmeans_table <- read.table("Set2-kmeans-partitions.txt")
n2 <- length(unlist(kmeans_table))
first <- vector()
for(i in kmeans_table) {
first <- append(first, paste("ete3 build -w standard_raxml -n /Users/David/Grive/Alfaro_Lab/Acanthomorpha/k-means_partitions/", i, sep = ""))
}
# Length check
length(first) - n2
# Remove file endings from the locus names
no_endings <- as.character(as.vector(as.matrix(kmeans_table)))
kmeans_names <- vector()
for(i in no_endings) {
kmeans_names <- append(kmeans_names, sapply(strsplit(i, split='.', fixed=TRUE), function(x) (x[1])))
}
kmeans_names
# Get the 2nd part of the command
second <- vector()
for(i in kmeans_names) {
second <- append(second, paste(" -o /Users/David/Grive/Alfaro_Lab/Acanthomorpha/k-means_partitions/", i, sep=""))
}
second
# Length check
length(second) - n2
# Get the entire command
kmeansscript <- paste(first, second, "-tree &&", sep="")
# Length check
length(kmeansscript) - n2
# Uncomment to print to file:
# write(kmeansscript, "kmeans-analysis.sh")Copy the trees into the directory containing the alignments:
# 1st part of the command
copytrees <- vector()
for(i in kmeans_names) {
copytrees <- append(copytrees, paste("cp /Users/David/Grive/Alfaro_Lab/Acanthomorpha/k-means_partitions/", i, sep = ""))
}
# 2nd part of the command
locations <- vector()
for(i in kmeans_names) {
locations <- append(locations, paste("-tree/clustalo_default-none-none-raxml_default/", i, sep = ""))
}
# Get the entire command
finalstep <- paste(copytrees, locations, ".phy.fasta.final_tree.nw /Users/David/Grive/Alfaro_Lab/Acanthomorpha/k-means_partitions/FASTA/ &&", sep="")
# Uncomment to print to file:
# write(finalstep, "copy-kmeans-trees.sh")Change the tree names so that they correspond to the partition names:
# 1st part of the command
oldtreename <- vector()
for(i in kmeans_names) {
oldtreename <- append(oldtreename, paste("mv /Users/David/Grive/Alfaro_Lab/Acanthomorpha/k-means_partitions/FASTA/", i, sep = ""))
}
# 2nd part of the command
newtreename <- vector()
for(i in kmeans_names) {
newtreename <- append(newtreename, paste(".phy.fasta.final_tree.nw /Users/David/Grive/Alfaro_Lab/Acanthomorpha/k-means_partitions/FASTA/", i, sep = ""))
}
# Get the entire command:
changetreenames <- paste(oldtreename, newtreename, ".tre &&", sep = "")
# Uncomment to print to file:
# write(changetreenames, "rename-kmeans-trees.sh")Finally, rename the partitions:
# 1st part of the command
oldpartition <- vector()
for(i in kmeans_names) {
oldpartition <- append(oldpartition, paste("mv /Users/David/Grive/Alfaro_Lab/Acanthomorpha/k-means_partitions/FASTA/", i, sep = ""))
}
# 2nd part of the command
newpartition <- vector()
for(i in kmeans_names) {
newpartition <- append(newpartition, paste(".phy.fasta /Users/David/Grive/Alfaro_Lab/Acanthomorpha/k-means_partitions/FASTA/", i, sep = ""))
}
# Get the entire command
renamepartitions <- paste(oldpartition, newpartition, ".fasta &&", sep = "")
# Uncomment to print to file:
# write(renamepartitions, "rename-partitions.sh")Phase 1: get_var_length
python src/get_var_length.py /Users/David/Grive/Alfaro_Lab/Acanthomorpha/k-means_partitions/FASTA/ --flend .tre --outf var-kmeans --outg alepisaurus_ferox,ceratoscopelus_warmingiiPhase 2: get_bp_genetrees
python src/get_bp_genetrees.py /Users/David/Grive/Alfaro_Lab/Acanthomorpha/k-means_partitions/FASTA/ /Users/David/Grive/Alfaro_Lab/Acanthomorpha/12_no_outgroups_scheme_3.tre --flend .tre --outf bp-kmeansPhase 3: combine_results
python src/combine_results.py var-kmeans bp-kmeans --outf comb-kmeansPhase 4: get_good_genes
In order of descending priority: bipartition support, root-to-tip variance, tree length
name root-to-tip_var treelength bipartition
8be7c94dcf2d71970c663f6710af40d7.tre 0.0878182 19.8649 0.560344827586
f495e4e0f2f9bbbf091f778067b062f4.tre 0.0230499 11.0301 0.508620689655
0061a6137d1029978876fe13239f57bc.tre 0.0205424 11.2184 0.5In order of descending priority: bipartition support, tree length, root-to-tip variance
name root-to-tip_var treelength bipartition
8be7c94dcf2d71970c663f6710af40d7.tre 0.0878182 19.8649 0.560344827586
f495e4e0f2f9bbbf091f778067b062f4.tre 0.0230499 11.0301 0.508620689655
0061a6137d1029978876fe13239f57bc.tre 0.0205424 11.2184 0.5In order of descending priority: root-to-tip variance, tree length, bipartition support
name root-to-tip_var treelength bipartition
4ff6dce0b7cebc9428b4b77e79356484.tre 0.000241636 1.15434 0.0258620689655
589d12bf910a9937d941aa4c836ad87d.tre 0.000935835 2.0223 0.336206896552
fe7aa5d9de0f5f51ef6365ef3b7f1e06.tre 0.00125304 2.60574 0.0603448275862In order of descending priority: root-to-tip variance, bipartition support, tree length
name root-to-tip_var treelength bipartition
4ff6dce0b7cebc9428b4b77e79356484.tre 0.000241636 1.15434 0.0258620689655
589d12bf910a9937d941aa4c836ad87d.tre 0.000935835 2.0223 0.336206896552
fe7aa5d9de0f5f51ef6365ef3b7f1e06.tre 0.00125304 2.60574 0.0603448275862In order of descending priority: tree length, root-to-tip variance, bipartition support
name root-to-tip_var treelength bipartition
4ff6dce0b7cebc9428b4b77e79356484.tre 0.000241636 1.15434 0.0258620689655
589d12bf910a9937d941aa4c836ad87d.tre 0.000935835 2.0223 0.336206896552
fe7aa5d9de0f5f51ef6365ef3b7f1e06.tre 0.00125304 2.60574 0.0603448275862In order of descending priority: tree length, bipartition support, root-to-tip variance
name root-to-tip_var treelength bipartition
4ff6dce0b7cebc9428b4b77e79356484.tre 0.000241636 1.15434 0.0258620689655
589d12bf910a9937d941aa4c836ad87d.tre 0.000935835 2.0223 0.336206896552
fe7aa5d9de0f5f51ef6365ef3b7f1e06.tre 0.00125304 2.60574 0.060344827586250-bp Chunks
A bash script was written to automate the following actions:
- Create a new directory nested in
/Users/David/Grive/Alfaro_Lab/Acanthomorpha/sate-gblocks-clean-min-114-taxa/FASTAfor each locus Split the alignments for each locus into individual sequences, and these into 50-bp chunks. This step was performed using a custom Python script obtained from http://www.reddit.com/r/bioinformatics/comments/1u8yc7/looking_for_a_script_that_will_split_dna/ceg8rav/?st=j0tbjfco&sh=1e300055.
''' Splits all sequences within a multi-fasta file into chunks of a specified size. Fasta header information is retained with each split sequence - its position in the original is appended to the id. Single-line and multi-line fasta files are supported. Prints to stout, so pipe to a file to store the result. Usage: python splitter.py <filename> <chunksize> python splitter.py myfile.fa 100 ''' from __future__ import print_function from sys import argv, version if version[0] == '2': from itertools import izip_longest as zl else: from itertools import zip_longest as zl chunksize = int(argv[2]) def writeseq(header, seq): for i, chunk in enumerate(zl(*[iter(seq)]*chunksize, fillvalue='')): print(header + '_{}bp'.format(i*chunksize)) print(''.join(chunk)) with open(argv[1]) as f: header, seq = f.readline().rstrip(), '' for l in f: if l[0] != '>': seq += l.rstrip() else: writeseq(header, seq) header, seq = l.rstrip(), '' writeseq(header, seq)For each locus, join the individual sequences chunk-wise (i.e., make a single fasta file for all taxa and sites 0 to 50, another one for all taxa and sites 51 to 100, etc.):
library(dplyr) # Alternating rows (name, sequence, name, sequence) go to two different columns, so that # each sequence is correctly assigned to its respective taxon: split_seqs <- read.table("split.txt") odd <- as.vector(split_seqs[seq(1, nrow(split_seqs), 2), ]) even <- as.vector(split_seqs[seq(2, nrow(split_seqs), 2), ]) odd_name <- "taxon" even_name <- "sequence" split_seqs_new <- data.frame(odd, even) names(split_seqs_new) <- c(odd_name, even_name) # Determine how long the locus is (i.e., how many 50-bp chunks it has been split into). # This can be done by counting the number of occurrences of a single taxon name. # In principle, any name could be used, but since not all of the UCEs include # all of the taxa, it is advisable to choose a taxon common to all the loci. n <- length(unique(grep("chaetodon_kleinii", split_seqs_new[,1], value = TRUE))) # Create a vector of strings that can filter taxon names according to the base pair range # tag attached to their end chunks <- vector() for(i in 0:(n-1)) { chunks <- append(chunks, paste("_", i*50, "bp", sep = "")) } # Create a list of data frames. Each element of the list represents a base pair range # and consists of a data frame containing both the "taxon" and "sequence" columns of # split_seqs_new partition <- list() for(i in 1:length(chunks)) { partition[[i]] <- data.frame(filter(split_seqs_new, grepl(as.character(chunks[i]), taxon))) } # Create a matrix whose columns represents individual chunks (i.e., base pair ranges) # and whose rows have the structure of the original split_seqs data frame -- i.e., name, # sequence, name, sequence: chunkmatrix <- matrix(ncol = length(partition), nrow = 2*(nrow(split_seqs_new)/length(partition))) for(i in 1:length(partition)) { for(j in 1:nrow(partition[[i]])) { chunkmatrix[(2*j - 1), i] <- as.character(partition[[i]][j, "taxon"]) chunkmatrix[2*j, i] <- as.character(partition[[i]][j, "sequence"]) } } # Print the resulting fasta files! for(i in 1:ncol(chunkmatrix)) { write(chunkmatrix[,i], paste("chunk_", i, ".fasta", sep = "")) }Add a locus-indicating prefix to all the chunks of a given UCE:
find *.fasta -maxdepth 0 ! -path . -exec mv {} uce-1005_{} \;Copy the resulting fasta files into a single directory.
The contents of the directory were then summarized as follows:
cd /Users/David/Grive/Alfaro_Lab/Acanthomorpha/sate-gblocks-clean-min-114-taxa/Chunks/
ls > /Users/David/Grive/Alfaro_Lab/SortaDate/Set3-50bp-chunks.txtNow, change the taxon names in the chunk FASTA files so that they correspond to the names in the reference tree. First, create a file with the names of all the chunk files:
cd /Users/David/Grive/Alfaro_Lab/Acanthomorpha/sate-gblocks-clean-min-114-taxa/Chunks/
ls *.fasta > /Users/David/Grive/Alfaro_Lab/Acanthomorpha/sate-gblocks-clean-min-114-taxa/Chunks/chunklist.txta <- read.table("/Users/David/Grive/Alfaro_Lab/Acanthomorpha/sate-gblocks-clean-min-114-taxa/Chunks/chunklist.txt")
x <- vector()
for(i in 1:nrow(a)) {
x <- append(x, paste("/Users/David/Grive/Alfaro_Lab/Acanthomorpha/sate-gblocks-clean-min-114-taxa/Chunks/", a[i,], sep = ""))
}
for(i in 1:length(x)) {
c <- read.table(print(x[i]), stringsAsFactors = FALSE)
d <- vector()
for(j in 1:(nrow(c)/2)) {
d[j] <- as.character(c[(2*j-1),])
}
e <- vector()
for(j in 1:length(d)) {
e[j] <- paste(sapply(strsplit(d[j], split="_", fixed=TRUE), function(x) (x[1])),
"_",
sapply(strsplit(d[j], split="_", fixed=TRUE), function(x) (x[2])),
sep = "")
}
for(j in 1:(nrow(c)/2)) {
c[(2*j-1),] <- e[j]
}
write(as.matrix(c), print(x[i]), ncolumns=1)
}A script was generated to analyze all of the alignment in the directory using RAxML:
chunk_table <- read.table("Set3-50bp-chunks.txt")
newfolders <- vector()
for(i in chunk_table) {
newfolders <- append(newfolders, paste("ete3 build -w standard_raxml -n /Users/David/Grive/Alfaro_Lab/Acanthomorpha/sate-gblocks-clean-min-114-taxa/Chunks/", i, sep=""))
}
with_endings <- as.character(as.vector(as.matrix(chunk_table)))
chunk_names <- vector()
for(i in with_endings) {
chunk_names <- append(chunk_names, sapply(strsplit(i, split='.', fixed=TRUE), function(x) (x[1])))
}
tree_location <- vector()
for(i in chunk_names) {
tree_location <- append(tree_location, paste(" -o /Users/David/Grive/Alfaro_Lab/Acanthomorpha/sate-gblocks-clean-min-114-taxa/", i, sep=""))
}
analyzechunks <- paste(newfolders, tree_location, sep="")
write(analyzechunks, "chunk-analysis.sh")The resulting tree files were then copied into the directory containing the alignments:
copyfrom1 <- vector()
for(i in chunk_names) {
copyfrom1 <- append(copyfrom1, paste("cp /Users/David/Grive/Alfaro_Lab/Acanthomorpha/sate-gblocks-clean-min-114-taxa/", i, sep = ""))
}
copyfrom2 <- vector()
for(i in chunk_names) {
copyfrom2 <- append(copyfrom2, paste("/clustalo_default-none-none-raxml_default/", i, sep = ""))
}
copyto <- paste(copyfrom1, copyfrom2, ".fasta.final_tree.nw /Users/David/Grive/Alfaro_Lab/Acanthomorpha/sate-gblocks-clean-min-114-taxa/Chunks/", sep="")
write(copyto, "copy-chunk-trees.sh")Rename the trees:
brew install rename
rename -S .fasta.final_tree.nw .tre *.fasta.final_tree.nwRunning ls *.fasta | wc -l and ls *.nw | wc -l in the directory showed that out of 1826 chunk FASTA files, no more than 1070 had tree files associated with them.
Phase 1: get_var_length
python src/get_var_length.py /Users/David/Grive/Alfaro_Lab/Acanthomorpha/sate-gblocks-clean-min-114-taxa/Chunks/ --flend .tre --outf Chunk_analysis/var-chunks --outg alepisaurus_ferox,ceratoscopelus_warmingiiPhase 2: get_bp_genetrees
python src/get_bp_genetrees.py /Users/David/Grive/Alfaro_Lab/Acanthomorpha/sate-gblocks-clean-min-114-taxa/Chunks/ /Users/David/Grive/Alfaro_Lab/Acanthomorpha/12_no_outgroups_scheme_3.tre --flend .tre --outf Chunk_analysis/bp-chunksPhase 3: combine_results
python src/combine_results.py Chunk_analysis/var-chunks Chunk_analysis/bp-chunks --outf Chunk_analysis/comb-chunksCalculate the Robinson-Foulds distances of the individual chunk trees from the reference tree using the Python script below:
import os, uuid
from ete3 import Tree
t2 = Tree("/Users/David/Grive/Alfaro_Lab/Acanthomorpha/12_no_outgroups_scheme_3.tre")
for file in os.listdir("/Users/David/Grive/Alfaro_Lab/Acanthomorpha/sate-gblocks-clean-min-114-taxa/Chunks/"):
if file.endswith(".tre"):
t1 = Tree(file)
try:
rf = t1.robinson_foulds(t2)
print str(file), (rf[0])
except:
pass
Step 1 of filtering: remove trees of zero length
No effect: the minimum observed tree length is non-zero and equal to:
## [1] 8.80081e-05Phase 4: get_good_genes
In order of descending priority: bipartition support, root-to-tip variance, tree length
name root-to-tip_var treelength bipartition
uce-157_chunk_7.tre 0.176994 14.918 0.258620689655
uce-126_chunk_3.tre 0.104056 14.0824 0.224137931034
uce-323_chunk_2.tre 1.16412 48.0104 0.224137931034## [1] "The Robinson-Foulds distances of the three bets chunks from the reference tree are as follows:"## chunk rf
## 321 uce-157_chunk_7.tre 164## chunk rf
## 219 uce-126_chunk_3.tre 166## chunk rf
## 443 uce-323_chunk_2.tre 168The chunk with the minimum RF distance from the reference tree:
## chunk rf
## 321 uce-157_chunk_7.tre 164In order of descending priority: bipartition support, tree length, root-to-tip variance
name root-to-tip_var treelength bipartition
uce-157_chunk_7.tre 0.176994 14.918 0.258620689655
uce-126_chunk_3.tre 0.104056 14.0824 0.224137931034
uce-323_chunk_2.tre 1.16412 48.0104 0.224137931034In order of descending priority: root-to-tip variance, tree length, bipartition support
name root-to-tip_var treelength bipartition
uce-1243_chunk_4.tre 1.05801e-11 0.0215722 0.0
uce-600_chunk_3.tre 1.28251e-11 0.0205929 0.00862068965517
uce-737_chunk_4.tre 2.16826e-11 0.0215345 0.0In order of descending priority: root-to-tip variance, bipartition support, tree length
name root-to-tip_var treelength bipartition
uce-1243_chunk_4.tre 1.05801e-11 0.0215722 0.0
uce-600_chunk_3.tre 1.28251e-11 0.0205929 0.00862068965517
uce-737_chunk_4.tre 2.16826e-11 0.0215345 0.0In order of descending priority: tree length, root-to-tip variance, bipartition support
name root-to-tip_var treelength bipartition
uce-855_chunk_6.tre 3.60737e-11 8.80081e-05 0.0
uce-413_chunk_4.tre 3.51022e-06 0.0205344 0.0
uce-600_chunk_3.tre 1.28251e-11 0.0205929 0.00862068965517In order of descending priority: tree length, bipartition support, root-to-tip variance
name root-to-tip_var treelength bipartition
uce-855_chunk_6.tre 3.60737e-11 8.80081e-05 0.0
uce-413_chunk_4.tre 3.51022e-06 0.0205344 0.0
uce-600_chunk_3.tre 1.28251e-11 0.0205929 0.0086206896551750-bp chunks: SH-like of 75% or above
Copy all the tree files to a new directory:
cd /Users/David/Grive/Alfaro_Lab/Acanthomorpha/sate-gblocks-clean-min-114-taxa mkdir Chunks_75 cp Chunks/*.tre Chunks_75Collapse all the nodes with SH-like support values of less than 75% using the following Python script:
import os, uuid from ete3 import Tree for file in os.listdir("/Users/David/Grive/Alfaro_Lab/Acanthomorpha/sate-gblocks-clean-min-114-taxa/Chunks"): if file.endswith(".tre"): outname = "/Users/David/Grive/Alfaro_Lab/Acanthomorpha/sate-gblocks-clean-min-114-taxa/Chunks_75/" + str(file) t = Tree(file, format=0) print t.get_ascii(attributes=['support', 'name']) for node in t.get_descendants(): if not node.is_leaf() and node.support <= 0.75: node.delete() print t.get_ascii(attributes=['support', 'name']) t.write(format=0, outfile=outname)Copy the fasta alignments to the same directory:
cp Chunks/*.fasta Chunks_75
Phase 1: get_var_length
python src/get_var_length.py /Users/David/Grive/Alfaro_Lab/Acanthomorpha/sate-gblocks-clean-min-114-taxa/Chunks_75/ --flend .tre --outf Chunk75_analysis/var-chunks75 --outg alepisaurus_ferox,ceratoscopelus_warmingiiPhase 2: get_bp_genetrees
python src/get_bp_genetrees.py /Users/David/Grive/Alfaro_Lab/Acanthomorpha/sate-gblocks-clean-min-114-taxa/Chunks_75/ /Users/David/Grive/Alfaro_Lab/Acanthomorpha/12_no_outgroups_scheme_3.tre --flend .tre --outf Chunk75_analysis/bp-chunks75Phase 3: combine_results
python src/combine_results.py Chunk75_analysis/var-chunks75 Chunk75_analysis/bp-chunks75 --outf Chunk75_analysis/comb-chunks75A script was written to delete all lines containing NAs, as well as all lines that only contained an entry for bipartition support but none for root-to-tip branch length variance or tree length. This was accomplished by filling these partially empty lines with NAs in the first step and deleting them in the second step:
combchunks75 <- read.table("/Users/David/Grive/Alfaro_Lab/SortaDate/Chunk75_analysis/comb-chunks75", fill = TRUE)
filtered <- na.omit(combchunks75)
write.table(filtered,
"/Users/David/Grive/Alfaro_Lab/SortaDate/Chunk75_analysis/comb-chunks75-filtered",
sep = "\t",
quote = FALSE,
row.names = FALSE,
col.names = FALSE)The length of the filtered comb file is 901 lines, meaning that SortaDate failed to obtain root-to-tip variance and/or tree length for 169 trees.
Step 1 of filtering: remove trees of zero length
No effect: the minimum observed tree length is non-zero and equal to:
## [1] 0.0202802Note that this result is orders of magnitude larger than those observed in the other five chunk datasets.
Phase 4: get_good_genes
In order of descending priority: bipartition support, root-to-tip variance, tree length
name root-to-tip_var treelength bipartition
uce-157_chunk_7.tre 0.163848 13.508 0.198275862069
uce-126_chunk_3.tre 0.101031 13.9331 0.181034482759
uce-323_chunk_2.tre 0.459664 40.9379 0.146551724138## [1] "The Robinson-Foulds distances of the three best chunks from the reference tree are as follows:"## chunk rf
## 284 uce-157_chunk_7.tre 126## chunk rf
## 194 uce-126_chunk_3.tre 114## chunk rf
## 391 uce-323_chunk_2.tre 125The chunk with the minimum RF distance from the reference tree:
## chunk rf
## 189 uce-1263_chunk_2.tre 106In order of descending priority: bipartition support, tree length, root-to-tip variance
name root-to-tip_var treelength bipartition
uce-157_chunk_7.tre 0.163848 13.508 0.198275862069
uce-126_chunk_3.tre 0.101031 13.9331 0.181034482759
uce-323_chunk_2.tre 0.459664 40.9379 0.146551724138In order of descending priority: root-to-tip variance, tree length, bipartition support
name root-to-tip_var treelength bipartition
uce-815_chunk_8.tre 0.0 0.0202802 0.0
uce-359_chunk_7.tre 0.0 0.0203467 0.00862068965517
uce-160_chunk_3.tre 0.0 0.0204413 0.0In order of descending priority: root-to-tip variance, bipartition support, tree length
name root-to-tip_var treelength bipartition
uce-200_chunk_2.tre 0.0 0.0317025 0.0344827586207
uce-129_chunk_5.tre 0.0 0.0211935 0.0258620689655
uce-1062_chunk_5.tre 0.0 0.113443 0.0258620689655In order of descending priority: tree length, root-to-tip variance, bipartition support
name root-to-tip_var treelength bipartition
uce-815_chunk_8.tre 0.0 0.0202802 0.0
uce-359_chunk_7.tre 0.0 0.0203467 0.00862068965517
uce-160_chunk_3.tre 0.0 0.0204413 0.0In order of descending priority: tree length, bipartition support, root-to-tip variance
name root-to-tip_var treelength bipartition
uce-815_chunk_8.tre 0.0 0.0202802 0.0
uce-359_chunk_7.tre 0.0 0.0203467 0.00862068965517
uce-160_chunk_3.tre 0.0 0.0204413 0.050-bp chunks: SH-like of 90% or above
(The first three steps were identical to those described above.)
Phase 1: get_var_length
python src/get_var_length.py /Users/David/Grive/Alfaro_Lab/Acanthomorpha/sate-gblocks-clean-min-114-taxa/Chunks_90/ --flend .tre --outf Chunk90_analysis/var-chunks90 --outg alepisaurus_ferox,ceratoscopelus_warmingiiPhase 2: get_bp_genetrees
python src/get_bp_genetrees.py /Users/David/Grive/Alfaro_Lab/Acanthomorpha/sate-gblocks-clean-min-114-taxa/Chunks_90/ /Users/David/Grive/Alfaro_Lab/Acanthomorpha/12_no_outgroups_scheme_3.tre --flend .tre --outf Chunk90_analysis/bp-chunks90Phase 3: combine_results
python src/combine_results.py Chunk90_analysis/var-chunks90 Chunk90_analysis/bp-chunks90 --outf Chunk90_analysis/comb-chunks90Delete the lines with NAs:
combchunks90 <- read.table("/Users/David/Grive/Alfaro_Lab/SortaDate/Chunk90_analysis/comb-chunks90", fill = TRUE)
filtered <- na.omit(combchunks90)
write.table(filtered,
"/Users/David/Grive/Alfaro_Lab/SortaDate/comb-chunks90-filtered",
sep = "\t",
quote = FALSE,
row.names = FALSE,
col.names = FALSE)The length of the filtered comb file is 536 lines, meaning that SortaDate failed to obtain root-to-tip variance and/or tree length for 534 (almost 50%) trees.
Step 1 of filtering: remove trees of zero length
No effect: the minimum observed tree length is non-zero and equal to:
## [1] 8.71304e-07Phase 4: get_good_genes
In order of descending priority: bipartition support, root-to-tip variance, tree length
name root-to-tip_var treelength bipartition
uce-1317_chunk_8.tre 0.0166339 15.2225 0.0689655172414
uce-120_chunk_6.tre 0.187599 4.6936 0.0689655172414
uce-323_chunk_2.tre 0.325194 35.4206 0.0689655172414## [1] "The Robinson-Foulds distances of the three best chunks from the reference tree are as follows:"## chunk rf
## 159 uce-1317_chunk_8.tre 111## chunk rf
## 81 uce-120_chunk_6.tre 114## chunk rf
## 248 uce-323_chunk_2.tre 107The chunk with the minimum RF distance from the reference tree:
## chunk rf
## 235 uce-267_chunk_7.tre 106In order of descending priority: bipartition support, tree length, root-to-tip variance
name root-to-tip_var treelength bipartition
uce-120_chunk_6.tre 0.187599 4.6936 0.0689655172414
uce-1317_chunk_8.tre 0.0166339 15.2225 0.0689655172414
uce-323_chunk_2.tre 0.325194 35.4206 0.0689655172414In order of descending priority: root-to-tip variance, tree length, bipartition support
name root-to-tip_var treelength bipartition
uce-537_chunk_3.tre 0.0 8.71304e-07 0.00862068965517
uce-84_chunk_5.tre 0.0 9.84452e-07 0.0172413793103
uce-739_chunk_2.tre 0.0 2.2254e-06 0.0344827586207In order of descending priority: root-to-tip variance, bipartition support, tree length
name root-to-tip_var treelength bipartition
uce-739_chunk_2.tre 0.0 2.2254e-06 0.0344827586207
uce-832_chunk_4.tre 0.0 1.05876 0.0258620689655
uce-84_chunk_5.tre 0.0 9.84452e-07 0.0172413793103In order of descending priority: tree length, root-to-tip variance, bipartition support
name root-to-tip_var treelength bipartition
uce-537_chunk_3.tre 0.0 8.71304e-07 0.00862068965517
uce-84_chunk_5.tre 0.0 9.84452e-07 0.0172413793103
uce-739_chunk_2.tre 0.0 2.2254e-06 0.0344827586207In order of descending priority: tree length, bipartition support, root-to-tip variance
name root-to-tip_var treelength bipartition
uce-537_chunk_3.tre 0.0 8.71304e-07 0.00862068965517
uce-84_chunk_5.tre 0.0 9.84452e-07 0.0172413793103
uce-739_chunk_2.tre 0.0 2.2254e-06 0.034482758620750-bp chunks from the 75%-complete dataset
A bash script used to split the UCE loci into 50-bp chunks was identical to that used above, with the exception of step 3 (joining all taxa represented by a given chunk into a new FASTA file), for which the following code was used:
library(dplyr)
# Alternating rows (name, sequence, name, sequence) go to two different columns, so that
# each sequence is correctly assigned to its respective taxon:
split_seqs <- read.table("split.txt")
odd <- as.vector(split_seqs[seq(1, nrow(split_seqs), 2), ])
even <- as.vector(split_seqs[seq(2, nrow(split_seqs), 2), ])
odd_name <- "taxon"
even_name <- "sequence"
split_seqs_new <- data.frame(odd, even)
names(split_seqs_new) <- c(odd_name, even_name)
# Determine how long the locus is (i.e., how many 50-bp chunks it has been split into).
# This can be done by counting the number of occurrences of a single taxon name.
# In principle, any name could be used, but since no taxon appears to be common to all
# the loci, the code below grabs the first taxon name appearing in the fasta file and
# counts its occurrences.
m <- paste(sapply(strsplit(as.character(split_seqs_new[1,1]), split="_", fixed=TRUE),
function(x) (x[1])),
"_", sapply(strsplit(as.character(split_seqs_new[1,1]), split="_", fixed=TRUE), function(x) (x[2])),
sep = "")
n <- length(unique(grep(m, split_seqs_new[,1], value = TRUE)))
# Create a vector of strings that can filter taxon names according to the base pair range
# tag attached to their end
chunks <- vector()
for(i in 0:(n-1)) {
chunks <- append(chunks, paste("_", i*50, "bp", sep = ""))
}
# Create a list of data frames. Each element of the list represents a base pair range
# and consists of a data frame containing both the "taxon" and "sequence" columns of
# split_seqs_new
partition <- list()
for(i in 1:length(chunks)) {
partition[[i]] <- data.frame(filter(split_seqs_new,
grepl(as.character(chunks[i]), taxon)))
}
# Create a matrix whose columns represents individual chunks (i.e., base pair ranges)
# and whose rows have the structure of the original split_seqs data frame -- i.e., name,
# sequence, name, sequence:
chunkmatrix <- matrix(ncol = length(partition),
nrow = 2*(nrow(split_seqs_new)/length(partition)))
for(i in 1:length(partition)) {
for(j in 1:nrow(partition[[i]])) {
chunkmatrix[(2*j - 1), i] <- as.character(partition[[i]][j, "taxon"])
chunkmatrix[2*j, i] <- as.character(partition[[i]][j, "sequence"])
}
}
# Print the resulting fasta files!
for(i in 1:ncol(chunkmatrix)) {
write(chunkmatrix[,i], paste("chunk_", i, ".fasta", sep = ""))
}The taxon names in the chunk FASTA files were then stripped of the chunk-indicating suffixes so as to correspond with the names used in the reference tree, and a script was used to analyze all the chunks using RAxML. The resulting trees were then copied into the directory containing the chunks. Out of 6543 chunk FASTA files, only 4237 had tree files associated with them, suggesting that RAxML failed to infer a tree for a given chunk in approx. 35% of cases.
Phase 1: get_var_length
python src/get_var_length.py /Users/David/Grive/Alfaro_Lab/Acanthomorpha/sate-gblocks-clean-min-90-taxa/Chunks/ --flend .tre --outf Min-90-chunk_analysis/var-min-90-chunks --outg alepisaurus_ferox,ceratoscopelus_warmingiiRunning the script produced 1029 warnings about either Alepisaurus ferox or Ceratoscopelus warmingii missing from the chunk tree. Despite this, the tree length and root-to-tip variance was computed for all the 4237 trees.
Phase 2: get_bp_genetrees
python src/get_bp_genetrees.py /Users/David/Grive/Alfaro_Lab/Acanthomorpha/sate-gblocks-clean-min-90-taxa/Chunks/ /Users/David/Grive/Alfaro_Lab/Acanthomorpha/12_no_outgroups_scheme_3.tre --flend .tre --outf Min-90-chunk_analysis/bp-min-90-chunksPhase 3: combine_results
python src/combine_results.py Min-90-chunk_analysis/var-min-90-chunks Min-90-chunk_analysis/bp-min-90-chunks --outf Min-90-chunk_analysis/comb-min-90-chunks
Phase 4: get_good_genes
In order of descending priority: bipartition support, root-to-tip variance, tree length
name root-to-tip_var treelength bipartition
uce-157_chunk_7.tre 0.176994 14.918 0.258620689655
uce-1244_chunk_1.tre 0.0534134 11.1586 0.224137931034
uce-126_chunk_3.tre 0.104056 14.0824 0.224137931034## [1] "The Robinson-Foulds distances of the three best chunks from the reference tree are as follows:"## chunk rf
## 1229 uce-157_chunk_7.tre 164## chunk rf
## 822 uce-1244_chunk_1.tre 158## chunk rf
## 907 uce-126_chunk_3.tre 166The chunk with the minimum RF distance from the reference tree:
## chunk rf
## 6 uce-1000_chunk_6.tre 136In order of descending priority: bipartition support, tree length, root-to-tip variance
name root-to-tip_var treelength bipartition
uce-157_chunk_7.tre 0.176994 14.918 0.258620689655
uce-1244_chunk_1.tre 0.0534134 11.1586 0.224137931034
uce-126_chunk_3.tre 0.104056 14.0824 0.224137931034In order of descending priority: root-to-tip variance, tree length, bipartition support
name root-to-tip_var treelength bipartition
uce-956_chunk_5.tre 2.3002e-12 8.56621e-05 0.0
uce-393_chunk_3.tre 5.54758e-12 9.22463e-05 0.0
uce-921_chunk_4.tre 5.8998e-12 7.89216e-05 0.0In order of descending priority: root-to-tip variance, bipartition support, tree length
name root-to-tip_var treelength bipartition
uce-956_chunk_5.tre 2.3002e-12 8.56621e-05 0.0
uce-393_chunk_3.tre 5.54758e-12 9.22463e-05 0.0
uce-921_chunk_4.tre 5.8998e-12 7.89216e-05 0.0In order of descending priority: tree length, root-to-tip variance, bipartition support
name root-to-tip_var treelength bipartition
uce-921_chunk_4.tre 5.8998e-12 7.89216e-05 0.0
uce-1173_chunk_3.tre 1.96604e-11 8.52591e-05 0.0
uce-956_chunk_5.tre 2.3002e-12 8.56621e-05 0.0In order of descending priority: tree length, bipartition support, root-to-tip variance
name root-to-tip_var treelength bipartition
uce-921_chunk_4.tre 5.8998e-12 7.89216e-05 0.0
uce-1173_chunk_3.tre 1.96604e-11 8.52591e-05 0.0
uce-956_chunk_5.tre 2.3002e-12 8.56621e-05 0.0Step 1 of filtering: remove trees of zero length
No effect: the minimum observed tree length is non-zero and equal to:
## [1] 7.89216e-05## Number of trees after filtering: 4237## Percentage of trees after filtering: 100%
Step 2 of filtering: remove the shortest and longest 10% of trees:
## Original min. tree length: 7.89216e-05## Original max. tree length: 214.974## Min. tree length after step 2: 0.285338## Max. tree length after step 2: 24.1424## Number of trees after step 2: 3389## Percentage of trees after step 2: 79.98584%
Step 3 of filtering: remove the most unclocklike third of loci
## Max. root-to-tip variance from step 2: 2.72349## Max. root-to-tip variance after step 3: 0.00905244## Number of trees after step 3: 2270## Percentage of trees after step 3: 53.57564%
Step 4 of filtering: keep the loci in the upper 10% of bipartition support
## Min. bipartition support from step 3: 0## Min. bipartition support after step 4: 0.1034483## Number of trees after step 4: 170## Percentage of trees after step 4: 4.012273%
50-bp chunks from the 75%-complete dataset: SH-like of 75% or above
The nodes with SH-like support values below 75% were collapsed using the script above. The corresponding FASTA files were copied into the resulting directory, and SortaDate was run as follows:
Phase 1: get_var_length
python src/get_var_length.py /Users/David/Grive/Alfaro_Lab/Acanthomorpha/sate-gblocks-clean-min-90-taxa/Chunks_75/ --flend .tre --outf Min-90-chunk75_analysis/var-min-90-chunks75 --outg alepisaurus_ferox,ceratoscopelus_warmingiiRunning the script resulted in occasional segmentation faults as well as “this really only works with nexus or newick” warning messages. The resulting file had the full number of lines (4237: one for each tree), but some of them were blank and others contained NAs.
Phase 2: get_bp_genetrees
python src/get_bp_genetrees.py /Users/David/Grive/Alfaro_Lab/Acanthomorpha/sate-gblocks-clean-min-90-taxa/Chunks_75/ /Users/David/Grive/Alfaro_Lab/Acanthomorpha/12_no_outgroups_scheme_3.tre --flend .tre --outf Min-90-chunk75_analysis/bp-min-90-chunks75Phase 3: combine_results
python src/combine_results.py Min-90-chunk75_analysis/var-min-90-chunks75 Min-90-chunk75_analysis/bp-min-90-chunks75 --outf Min-90-chunk75_analysis/comb-min-90-chunks75Delete the lines with NAs:
comb90chunks75 <- read.table("/Users/David/Grive/Alfaro_Lab/SortaDate/Min-90-chunk75_analysis/comb-min-90-chunks75", fill = TRUE)
filtered <- na.omit(comb90chunks75)
write.table(filtered,
"/Users/David/Grive/Alfaro_Lab/SortaDate/Min-90-chunk75_analysis/comb-min-90-chunks75-filtered",
sep = "\t",
quote = FALSE,
row.names = FALSE,
col.names = FALSE)
nrow(filtered)The length of the filtered comb file is 3589 lines, meaning that SortaDate failed to obtain root-to-tip variance and/or tree length for 648 trees.
Phase 4: get_good_genes
In order of descending priority: bipartition support, root-to-tip variance, tree length
name root-to-tip_var treelength bipartition
uce-157_chunk_7.tre 0.163848 13.508 0.198275862069
uce-1244_chunk_1.tre 0.040797 10.3002 0.181034482759
uce-126_chunk_3.tre 0.101031 13.9331 0.181034482759## [1] "The Robinson-Foulds distances of the three best chunks from the reference tree are as follows:"## chunk rf
## 1064 uce-157_chunk_7.tre 126## chunk rf
## 710 uce-1244_chunk_1.tre 111## chunk rf
## 788 uce-126_chunk_3.tre 114The chunk with the minimum RF distance from the reference tree:
## chunk rf
## 2016 uce-463_chunk_2.tre 85In order of descending priority: bipartition support, tree length, root-to-tip variance
name root-to-tip_var treelength bipartition
uce-157_chunk_7.tre 0.163848 13.508 0.198275862069
uce-1244_chunk_1.tre 0.040797 10.3002 0.181034482759
uce-126_chunk_3.tre 0.101031 13.9331 0.181034482759In order of descending priority: root-to-tip variance, tree length, bipartition support
name root-to-tip_var treelength bipartition
uce-1175_chunk_4.tre 0.0 8.48388e-07 0.00862068965517
uce-547_chunk_2.tre 0.0 1.18678e-06 0.0172413793103
uce-815_chunk_8.tre 0.0 0.0202802 0.0In order of descending priority: root-to-tip variance, bipartition support, tree length
name root-to-tip_var treelength bipartition
uce-67_chunk_4.tre 0.0 0.0214551 0.0603448275862
uce-200_chunk_2.tre 0.0 0.0317025 0.0344827586207
uce-525_chunk_3.tre 0.0 0.020706 0.0258620689655In order of descending priority: tree length, root-to-tip variance, bipartition support
name root-to-tip_var treelength bipartition
uce-1175_chunk_4.tre 0.0 8.48388e-07 0.00862068965517
uce-547_chunk_2.tre 0.0 1.18678e-06 0.0172413793103
uce-815_chunk_8.tre 0.0 0.0202802 0.0In order of descending priority: tree length, bipartition support, root-to-tip variance
name root-to-tip_var treelength bipartition
uce-1175_chunk_4.tre 0.0 8.48388e-07 0.00862068965517
uce-547_chunk_2.tre 0.0 1.18678e-06 0.0172413793103
uce-815_chunk_8.tre 0.0 0.0202802 0.0Step 1 of filtering: remove trees of zero length
No effect: the minimum observed tree length is non-zero and equal to:
## [1] 8.48388e-07## Number of trees after filtering: 3589## Percentage of trees after filtering: 100%
Step 2 of filtering: remove the shortest and longest 10% of trees:
## Original min. tree length: 8.48388e-07## Original max. tree length: 209.395## Min. tree length after step 2: 0.124261## Max. tree length after step 2: 19.679## Number of trees after step 2: 2871## Percentage of trees after step 2: 79.99443%
Step 3 of filtering: remove the most unclocklike third of loci
## Max. root-to-tip variance from step 2: 6.66986## Max. root-to-tip variance after step 3: 0.00645959## Number of trees after step 3: 1923## Percentage of trees after step 3: 53.58038%
Step 4 of filtering: keep the loci in the upper 10% of bipartition support
## Min. bipartition support from step 3: 0## Min. bipartition support after step 4: 0.06034483## Number of trees after step 4: 169## Percentage of trees after step 4: 4.708833%
50-bp chunks from the 75%-complete dataset: SH-like of 90% or above
Phase 1: get_var_length
python src/get_var_length.py /Users/David/Grive/Alfaro_Lab/Acanthomorpha/sate-gblocks-clean-min-90-taxa/Chunks_90/ --flend .tre --outf Min-90-chunk90_analysis/var-min-90-chunks90 --outg alepisaurus_ferox,ceratoscopelus_warmingiiAs in the previous case, the command led to a number of segfaults and “this really only works with nexus or newick” warnings, corresponding to lines in the resulting file that were either incomplete or included NAs.
Phase 2: get_bp_genetrees
python src/get_bp_genetrees.py /Users/David/Grive/Alfaro_Lab/Acanthomorpha/sate-gblocks-clean-min-90-taxa/Chunks_90/ /Users/David/Grive/Alfaro_Lab/Acanthomorpha/12_no_outgroups_scheme_3.tre --flend .tre --outf Min-90-chunk90_analysis/bp-min-90-chunks90Phase 3: combine_results
python src/combine_results.py Min-90-chunk90_analysis/var-min-90-chunks90 Min-90-chunk90_analysis/bp-min-90-chunks90 --outf Min-90-chunk90_analysis/comb-min-90-chunks90Delete the lines with NAs:
comb90chunks90 <- read.table("/Users/David/Grive/Alfaro_Lab/SortaDate/Min-90-chunk90_analysis/comb-min-90-chunks90", fill = TRUE)
filtered <- na.omit(comb90chunks90)
write.table(filtered,
"/Users/David/Grive/Alfaro_Lab/SortaDate/Min-90-chunk90_analysis/comb-min-90-chunks90-filtered",
sep = "\t",
quote = FALSE,
row.names = FALSE,
col.names = FALSE)
nrow(filtered)The length of the filtered comb file is 2319 lines, meaning that SortaDate failed to obtain root-to-tip variance and/or tree length for 1918 trees.
Phase 4: get_good_genes
In order of descending priority: bipartition support, root-to-tip variance, tree length
name root-to-tip_var treelength bipartition
uce-625_chunk_10.tre 0.0675117 0.706842 0.0775862068966
uce-1317_chunk_8.tre 0.0166339 15.2225 0.0689655172414
uce-120_chunk_6.tre 0.187599 4.6936 0.0689655172414## [1] "The Robinson-Foulds distances of the three best chunks from the reference tree are as follows:"## chunk rf
## 1686 uce-625_chunk_10.tre 106## chunk rf
## 629 uce-1317_chunk_8.tre 111## chunk rf
## 429 uce-120_chunk_6.tre 114The chunk with the minimum RF distance from the reference tree:
## chunk rf
## 678 uce-1340_chunk_2.tre 85In order of descending priority: bipartition support, tree length, root-to-tip variance
name root-to-tip_var treelength bipartition
uce-625_chunk_10.tre 0.0675117 0.706842 0.0775862068966
uce-120_chunk_6.tre 0.187599 4.6936 0.0689655172414
uce-1317_chunk_8.tre 0.0166339 15.2225 0.0689655172414In order of descending priority: root-to-tip variance, tree length, bipartition support
name root-to-tip_var treelength bipartition
uce-1175_chunk_4.tre 0.0 8.48388e-07 0.00862068965517
uce-675_chunk_5.tre 0.0 8.58914e-07 0.0
uce-537_chunk_3.tre 0.0 8.71304e-07 0.00862068965517In order of descending priority: root-to-tip variance, bipartition support, tree length
name root-to-tip_var treelength bipartition
uce-1189_chunk_2.tre 0.0 1.52836 0.0431034482759
uce-739_chunk_2.tre 0.0 2.2254e-06 0.0344827586207
uce-67_chunk_4.tre 0.0 0.0214551 0.0344827586207In order of descending priority: tree length, root-to-tip variance, bipartition support
name root-to-tip_var treelength bipartition
uce-1175_chunk_4.tre 0.0 8.48388e-07 0.00862068965517
uce-675_chunk_5.tre 0.0 8.58914e-07 0.0
uce-537_chunk_3.tre 0.0 8.71304e-07 0.00862068965517In order of descending priority: tree length, bipartition support, root-to-tip variance
name root-to-tip_var treelength bipartition
uce-1175_chunk_4.tre 0.0 8.48388e-07 0.00862068965517
uce-675_chunk_5.tre 0.0 8.58914e-07 0.0
uce-537_chunk_3.tre 0.0 8.71304e-07 0.00862068965517Step 1 of filtering: remove trees of zero length
No effect: the minimum observed tree length is non-zero and equal to:
## [1] 8.48388e-07## Number of trees after filtering: 2319## Percentage of trees after filtering: 100%
Step 2 of filtering: remove the shortest and longest 10% of trees:
## Original min. tree length: 8.48388e-07## Original max. tree length: 141.225## Min. tree length after step 2: 0.111172## Max. tree length after step 2: 20.8823## Number of trees after step 2: 1855## Percentage of trees after step 2: 79.99138%
Step 3 of filtering: remove the most unclocklike third of loci
## Max. root-to-tip variance from step 2: 40.1753## Max. root-to-tip variance after step 3: 0.00177898## Number of trees after step 3: 1243## Percentage of trees after step 3: 53.60069%
Step 4 of filtering: keep the loci in the upper 10% of bipartition support
## Min. bipartition support from step 3: 0## Min. bipartition support after step 4: 0.03448276## Number of trees after step 4: 66## Percentage of trees after step 4: 2.846054%
Out of the 66 chunks selected from the SH-like > 0.9 dataset, 65 were 50-bp long. To facilitate PartitionFinder searches, these chunks were aligned first using SequenceMatrix v1.8, with the last (47-bp) chunk attached to the resulting alignment afterwards. This made it easier to automate the calculation of base pair ranges for each locus (the latter needed to be included in the PartitionFinder configuration file). The outgroups (Alepisaurus ferox and Ceratoscopelus warmingii) were excluded from the concatenated alignment, as they were not present in the available topology constraint.
PartitionFinder was first run with the “rcluster” search option. Although the “models” option was set to “all”, since rcluster searches can only be performed in RAxML, this effectively limited the analysis to the three models implemented in the latter software (GTR, GTR+\(\Gamma\), GTR+\(\Gamma\)+I). This run yielded 8 subsets of the following properties:
| Subset | Best Model | Sites | Rate under GTR+\(\Gamma\) | ID |
|---|---|---|---|---|
| 1 | GTR+\(\Gamma\) | 1047 | 1.048826 | 76dec7513c9b37738bfac30d5d512cf3 |
| 2 | GTR+I+\(\Gamma\) | 1100 | 0.672610 | 111ec939b8c23b0e4e2529cee50f387a |
| 3 | GTR+\(\Gamma\) | 550 | 0.281570 | 00fea7ffef92b99d78df6650b56ebb0c |
| 4 | GTR+\(\Gamma\) | 100 | 0.965800 | 065a20f09eb1fa7383dedb55df51e100 |
| 5 | GTR+\(\Gamma\) | 200 | 2.278670 | a6afbf36fc27e68c089e09c31ca3c71f |
| 6 | GTR+\(\Gamma\) | 50 | 1.682760 | da54ec6f6966e6b696433ef124df9e1f |
| 7 | GTR+I+\(\Gamma\) | 50 | 2.739838 | 2d0d981211d9b6e3f364c1fcd221b555 |
| 8 | GTR+\(\Gamma\) | 200 | 1.356237 | fd9894d33a357dcdab10c55c5153eb16 |
In the next step, the alignment was analyzed under the “greedy” search option, with the BEAST model set and without the “–raxml” flag. This search recovered the following 14 partitions:
| Subset | Best Model | Sites | Tree size under the best model | ID |
|---|---|---|---|---|
| 1 | HKY+\(\Gamma\)+X | 100 | 6.07552 | 3f353939a600a6973a6a1e0608925da6 |
| 2 | TRNEF+\(\Gamma\) | 200 | 4.34326 | ceefdb63d1de1122404ab11285f05f27 |
| 3 | JC+\(\Gamma\) | 50 | 2.80253 | ac2a21040350ec54b4664bc752a13a45 |
| 4 | TRNEF+\(\Gamma\) | 300 | 1.53147 | baed377a41689244a752bc9e3169c975 |
| 5 | GTR+I+\(\Gamma\)+X | 350 | 3.44224 | ce05da9c4ae84b4a74cb468b18fcda82 |
| 6 | K80+I+\(\Gamma\) | 450 | 3.71596 | 16b9f71868c90950d0dd144fd5de511f |
| 7 | SYM+\(\Gamma\) | 300 | 8.15389 | c5076d62640bd9eeabe53de6e5bf7f7a |
| 8 | K80+\(\Gamma\) | 150 | 9.20524 | d5aeca1d1fad19fc89dbb336bad98cbd |
| 9 | K80+\(\Gamma\) | 497 | 5.13898 | e7f992b256534832ccdb46c09aa6e9a4 |
| 10 | HKY+\(\Gamma\)+X | 300 | 2.87180 | 4e722846245f8cd6600c32f3131120a6 |
| 11 | TRNEF+\(\Gamma\) | 250 | 5.87708 | 72ad9849e1f3be2cbffa1097935e00fc |
| 12 | GTR+I+\(\Gamma\)+X | 50 | 36.99851 | 2d0d981211d9b6e3f364c1fcd221b555 |
| 13 | JC+\(\Gamma\) | 150 | 1.20339 | d687ea29d8ab88ed27334eafc8164ec8 |
| 14 | TRNEF+\(\Gamma\) | 150 | 13.44953 | 00f862e3cabdf1be1b99bc5b1455ba78 |
(In PhyML, “tree size” denotes the sum of edge lengths: see http://github.com/stephaneguindon/phyml/blob/master/doc/phyml-manual.tex. “X” denotes estimated, as opposed to empirical, base frequencies.)
BEAST analysis
The two 50-bp partitions were removed. Each partition received its own substitution model, which was generally identical to that selected by PartitionFinder, except for those models that contained the I parameter (proportion of invariant sites). Since this parameter and \(\Gamma\) account for the same phenomenon (rate heterogeneity across sites), their simultaneous inclusion causes the resulting model to be non-identifiable, leading to potential mixing problems in MCMC simulations. The models that were not directly available in BEAUTi were implemented as follows:
- TrNef: TN93 + equal base frequencies
- K80: HKY85 + equal base frequencies
- SYM: GTR + equal base frequencies
Each \(\Gamma\) rate heterogeneity distribution was discretized into 4 categories. The default improper prior on the relative rates (the allMus parameter) was set to a gamma distribution with a shape of 0.001 and a scale of 1000 following the recommendations at https://www.biostat.washington.edu/sites/default/files/modules//2016_SISMID_13_11.pdf. All the priors on substitution model parameters were kept at their default values.
In contrast to the substitution models, the parameters of the uncorrelated lognormal clock model were linked across partitions. The ucld.mean hyperparameter was assigned a lognormal hyperprior with a mean of 0.005 (in real space) and a log standard deviation of 1, with the initial value set equal to 0.005. A truncated exponential hyperprior with support (0, 1), a mean of 0.3, and an initial value of 0.1 was used for ucld.stdev.
The “fixed tree topology” operator mix was used (based on a user-supplied topology common to all partitions), with the tuning of the ucld.mean and ucld.stdev operators set to 0.9 (default value = 0.75) and their weight doubled (from 3.0 to 6.0). Default tuning values and weights were used for all the remaining operators.
The 12 internal calibrations were all implemented as exponentials, and the (Polymixia + Aphredoderus) received the corrected calibration whose 95th percentile was equal to 116.35 Ma. A truncated exponential distribution supported on (98 Ma, 143 Ma) was constructed to calibrate the root of the tree, with its mean equal to the midpoint of the support interval (22.5 without the offset).
Finally, the MCMC simulation was run for 500 million generations, with a sampling period of 1000 generations.
beast -threads 12 -beagle_SSE concatchunks.xmlThe analysis finished after 12.597 days and the resulting log file was examined using LogAnalyser to determine the EES value of each parameter:
java -Xmx6000m -cp /home/analysis/.linuxbrew/lib/beast.jar dr.app.tools.LogAnalyser -burnin 50000000 -ess concatchunks.log## statistic median ESS
## 1 posterior -48625.69180000 3413.0600
## 2 prior -2049.74610000 844.2895
## 3 likelihood -46575.83370000 32241.1992
## 4 treeModel.rootHeight 137.72570000 2238.3418
## 5 tmrca(Aipichthys) 120.61650000 1324.2513
## 6 tmrca(Berybolcensis) 55.65810000 6036.6497
## 7 tmrca(Calatomus) 23.59360000 718.9452
## 8 tmrca(Chaetodontidae) 37.91700000 6567.4848
## 9 tmrca(Eastmanalepes) 50.96060000 16986.7366
## 10 tmrca(Eobuglossus) 43.01970000 8226.7418
## 11 tmrca(Eucoelopoma) 56.77560000 5699.0608
## 12 tmrca(Homonotichthys) 98.71850000 1207.3403
## 13 tmrca(Mcconichthys) 66.97340000 688.1744
## 14 tmrca(Mene) 59.19900000 4229.6624
## 15 tmrca(Ramphexocoetus) 53.71620000 3420.8121
## 16 tmrca(Root) 137.72570000 2238.3418
## 17 tmrca(Tarkus) 49.83070000 13887.1125
## 18 birthDeath.meanGrowthRate 0.01870000 3785.5560
## 19 birthDeath.relativeDeathRate 0.05030000 125120.0000
## 20 Subset1HKYGX.kappa 3.30330000 336740.0000
## 21 Subset1HKYGX.frequencies1 0.38220000 160000.0000
## 22 Subset1HKYGX.frequencies2 0.23020000 194740.0000
## 23 Subset1HKYGX.frequencies3 0.15200000 188220.0000
## 24 Subset1HKYGX.frequencies4 0.23390000 191870.0000
## 25 Subset1HKYGX.alpha 0.98970000 344070.0000
## 26 Subset2TRNEFG.kappa1 4.82390000 339050.0000
## 27 Subset2TRNEFG.kappa2 1.81510000 353590.0000
## 28 Subset2TRNEFG.alpha 0.67370000 343600.0000
## 29 Subset3TRNEFG.kappa1 3.05060000 330620.0000
## 30 Subset3TRNEFG.kappa2 4.86070000 327930.0000
## 31 Subset3TRNEFG.alpha 0.46710000 335870.0000
## 32 Subset4GTRIGX.ac 0.17860000 142440.0000
## 33 Subset4GTRIGX.ag 0.41800000 69343.9763
## 34 Subset4GTRIGX.at 0.10600000 158030.0000
## 35 Subset4GTRIGX.cg 0.34480000 106840.0000
## 36 Subset4GTRIGX.gt 0.14930000 132440.0000
## 37 Subset4GTRIGX.frequencies1 0.30180000 109180.0000
## 38 Subset4GTRIGX.frequencies2 0.21230000 87820.5730
## 39 Subset4GTRIGX.frequencies3 0.31520000 91691.2293
## 40 Subset4GTRIGX.frequencies4 0.16960000 98336.1071
## 41 Subset4GTRIGX.alpha 0.44590000 276290.0000
## 42 Subset5K80IG.kappa 3.56400000 364980.0000
## 43 Subset5K80IG.alpha 0.31900000 370210.0000
## 44 Subset6SYMG.ac 0.31450000 233040.0000
## 45 Subset6SYMG.ag 1.61400000 171440.0000
## 46 Subset6SYMG.at 0.24760000 241230.0000
## 47 Subset6SYMG.cg 1.23840000 197650.0000
## 48 Subset6SYMG.gt 0.24490000 285190.0000
## 49 Subset6SYMG.alpha 0.52120000 361230.0000
## 50 Subset7K80G.kappa 2.93730000 365130.0000
## 51 Subset7K80G.alpha 0.70830000 361470.0000
## 52 Subset8K80G.kappa 3.17480000 373600.0000
## 53 Subset8K80G.alpha 1.22040000 364000.0000
## 54 Subset9HKYGX.kappa 3.59770000 358280.0000
## 55 Subset9HKYGX.frequencies1 0.20910000 185070.0000
## 56 Subset9HKYGX.frequencies2 0.25900000 189260.0000
## 57 Subset9HKYGX.frequencies3 0.30580000 174970.0000
## 58 Subset9HKYGX.frequencies4 0.22510000 192860.0000
## 59 Subset9HKYGX.alpha 0.62780000 344070.0000
## 60 Subset10TRNEFG.kappa1 4.17550000 337400.0000
## 61 Subset10TRNEFG.kappa2 6.02560000 342470.0000
## 62 Subset10TRNEFG.alpha 0.50600000 338290.0000
## 63 Subset11JCG.alpha 0.38770000 282140.0000
## 64 Subset12TRNEFG.kappa1 3.08980000 324920.0000
## 65 Subset12TRNEFG.kappa2 7.49850000 321000.0000
## 66 Subset12TRNEFG.alpha 0.33910000 220060.0000
## 67 Subset1HKYGX.mu 1.17860000 183520.0000
## 68 Subset2TRNEFG.mu 0.86830000 155210.0000
## 69 Subset3TRNEFG.mu 0.28780000 112480.0000
## 70 Subset4GTRIGX.mu 0.71110000 105770.0000
## 71 Subset5K80IG.mu 0.81240000 89865.7064
## 72 Subset6SYMG.mu 1.65800000 86079.4311
## 73 Subset7K80G.mu 1.92930000 131010.0000
## 74 Subset8K80G.mu 1.05670000 92400.6821
## 75 Subset9HKYGX.mu 0.59180000 131650.0000
## 76 Subset10TRNEFG.mu 1.18620000 132450.0000
## 77 Subset11JCG.mu 0.20350000 110950.0000
## 78 Subset12TRNEFG.mu 2.54390000 79083.6503
## 79 ucld.mean 0.00073594 332.5618
## 80 ucld.stdev 0.98430000 2649.2935
## 81 meanRate 0.00068979 700.5540
## 82 coefficientOfVariation 1.27000000 226.7723
## 83 covariance 0.23290000 433.6862
## 84 Subset1HKYGX.treeLikelihood -1890.61200000 36770.2075
## 85 Subset2TRNEFG.treeLikelihood -2931.73400000 35236.5308
## 86 Subset3TRNEFG.treeLikelihood -2228.42960000 36860.1607
## 87 Subset4GTRIGX.treeLikelihood -4743.64810000 29583.0309
## 88 Subset5K80IG.treeLikelihood -5280.81190000 32435.6649
## 89 Subset6SYMG.treeLikelihood -5733.31740000 32350.1559
## 90 Subset7K80G.treeLikelihood -3537.00090000 26073.9083
## 91 Subset8K80G.treeLikelihood -8747.29310000 18666.6737
## 92 Subset9HKYGX.treeLikelihood -3646.88110000 24260.8424
## 93 Subset10TRNEFG.treeLikelihood -4255.05270000 39038.3017
## 94 Subset11JCG.treeLikelihood -949.70740000 9002.7411
## 95 Subset12TRNEFG.treeLikelihood -2630.61690000 25585.5111
## 96 branchRates 0.00000000 1000.0000
## 97 speciation -580.10770000 566.5915Since all the ESS values exceeded 200, the sample from the posterior was summarized using TreeAnnotator:
java -Xmx20000m -cp /home/analysis/.linuxbrew/lib/beast.jar dr.app.tools.TreeAnnotator -burnin 50000000 -heights median concatchunks.trees chunks-BEAST.tre
Comparison with the MCMCTree-generated tree from the manuscript
BEAST: Random local clocks
Two more analyses were performed using BEAST; their settings were identical to those described above except for the clock model used. In both cases, a random local clock model was utilized, with one analysis using the uncorrelated version of the model (with the ratesAreMultipliers option in the XML file set to the default value of "false") and the other employing the correlated version (ratesAreMultipliers="true"). In both analyses, default priors were placed on the parameters of the clock model.
beast -threads 6 -beagle_SSE concatchunks-urlc.xmlbeast -threads 6 -beagle_SSE concatchunks-crlc.xml(Note: these chains did not reach the target length due to a power outage.)
BEAST: Extended calibration set
Another analysis was started with settings identical to the first one (in particular, with the uncorrelated lognormal relaxed clock model), but with the extended dataset of 28 calibrations:
/home/linuxbrew/.linuxbrew/bin/beast -beagle_SSE -beagle_scaling dynamic concatchunks-ext.xmlThis run finished after 7.059 days, with the following effective sample sizes for individual parameters (as determined by LogAnalyser):
/home/linuxbrew/.linuxbrew/bin/loganalyser -burnin 50000000 -ess concatchunks-ext.log## statistic median ESS
## 1 posterior -48684.38740000 2763.4231
## 2 prior -2109.18830000 721.1500
## 3 likelihood -46575.16520000 13844.4609
## 4 treeModel.rootHeight 136.14570000 1743.5335
## 5 tmrca(Aipichthys) 122.76580000 1222.0035
## 6 tmrca(Archaeotetraodon) 36.63320000 198.8701
## 7 tmrca(Archaeus) 56.60770000 8352.3874
## 8 tmrca(Berybolcensis) 54.67900000 7254.2808
## 9 tmrca(Calatomus) 26.00620000 399.8151
## 10 tmrca(Chaetodontidae) 41.24440000 6296.0075
## 11 tmrca(Cretzeus) 94.24210000 135.2974
## 12 tmrca(Eastmanalepes) 52.64200000 14885.0059
## 13 tmrca(Eobothus) 57.80940000 1785.4873
## 14 tmrca(Eobuglossus) 43.29610000 4816.3060
## 15 tmrca(Eocoelopoma) 56.88220000 5205.1277
## 16 tmrca(Gasterorhamphosus) 75.07320000 2132.2268
## 17 tmrca(Homonotichthys) 99.30460000 1329.7146
## 18 tmrca(Hoplopteryx) 127.44200000 1086.9194
## 19 tmrca(Luvarus) 65.22740000 809.0498
## 20 tmrca(Mahengechromis) 48.20490000 7659.4325
## 21 tmrca(Massamorichthys) 63.21960000 600.5667
## 22 tmrca(Mene) 58.76210000 4622.4281
## 23 tmrca(Proacanthurus) 51.23210000 6784.3879
## 24 tmrca(Prosolenostomus) 59.10850000 821.7280
## 25 tmrca(Rhamphexocoetus) 54.45010000 6244.0991
## 26 tmrca(Rhinocephalus) 56.57650000 2295.8754
## 27 tmrca(Root) 136.14570000 1743.5335
## 28 tmrca(Sphyraena) 59.69350000 8346.0325
## 29 tmrca(Tarkus) 49.83670000 9320.9490
## 30 tmrca(Trachipterus) 25.06320000 870.6285
## 31 tmrca(Triodon) 53.63450000 612.9674
## 32 tmrca(Turkmene) 64.00440000 855.3938
## 33 tmrca(Zenopsis) 37.80840000 420.6294
## 34 birthDeath.meanGrowthRate 0.01850000 3892.3232
## 35 birthDeath.relativeDeathRate 0.04550000 15134.3519
## 36 Subset1HKYGX.kappa 3.30200000 18001.0000
## 37 Subset1HKYGX.frequencies1 0.38230000 18001.0000
## 38 Subset1HKYGX.frequencies2 0.23000000 18001.0000
## 39 Subset1HKYGX.frequencies3 0.15230000 18001.0000
## 40 Subset1HKYGX.frequencies4 0.23400000 18001.0000
## 41 Subset1HKYGX.alpha 0.98980000 17491.3422
## 42 Subset2TRNEFG.kappa1 4.81920000 18001.0000
## 43 Subset2TRNEFG.kappa2 1.81260000 17951.5243
## 44 Subset2TRNEFG.alpha 0.67220000 17988.8690
## 45 Subset3TRNEFG.kappa1 3.04990000 17514.5608
## 46 Subset3TRNEFG.kappa2 4.84920000 18001.0000
## 47 Subset3TRNEFG.alpha 0.46630000 17907.2312
## 48 Subset4GTRIGX.ac 0.17760000 18001.0000
## 49 Subset4GTRIGX.ag 0.41520000 17530.1025
## 50 Subset4GTRIGX.at 0.10570000 18001.0000
## 51 Subset4GTRIGX.cg 0.34220000 17165.8646
## 52 Subset4GTRIGX.gt 0.14880000 17568.1246
## 53 Subset4GTRIGX.frequencies1 0.30190000 17366.2591
## 54 Subset4GTRIGX.frequencies2 0.21200000 17969.0065
## 55 Subset4GTRIGX.frequencies3 0.31540000 17187.6766
## 56 Subset4GTRIGX.frequencies4 0.16930000 17966.0438
## 57 Subset4GTRIGX.alpha 0.44540000 18001.0000
## 58 Subset5K80IG.kappa 3.56170000 18001.0000
## 59 Subset5K80IG.alpha 0.31900000 18001.0000
## 60 Subset6SYMG.ac 0.31350000 16789.4178
## 61 Subset6SYMG.ag 1.60860000 18001.0000
## 62 Subset6SYMG.at 0.24720000 17544.8122
## 63 Subset6SYMG.cg 1.23730000 17490.5724
## 64 Subset6SYMG.gt 0.24400000 18001.0000
## 65 Subset6SYMG.alpha 0.51970000 18001.0000
## 66 Subset7K80G.kappa 2.93720000 18001.0000
## 67 Subset7K80G.alpha 0.70890000 18001.0000
## 68 Subset8K80G.kappa 3.17600000 17757.1075
## 69 Subset8K80G.alpha 1.21870000 17392.0732
## 70 Subset9HKYGX.kappa 3.59600000 17229.8960
## 71 Subset9HKYGX.frequencies1 0.20940000 17411.0904
## 72 Subset9HKYGX.frequencies2 0.25880000 18001.0000
## 73 Subset9HKYGX.frequencies3 0.30590000 17833.6630
## 74 Subset9HKYGX.frequencies4 0.22510000 17823.9222
## 75 Subset9HKYGX.alpha 0.62670000 17827.3347
## 76 Subset10TRNEFG.kappa1 4.17290000 17254.1945
## 77 Subset10TRNEFG.kappa2 6.01360000 18001.0000
## 78 Subset10TRNEFG.alpha 0.50490000 17516.4006
## 79 Subset11JCG.alpha 0.38730000 18001.0000
## 80 Subset12TRNEFG.kappa1 3.08330000 17424.3666
## 81 Subset12TRNEFG.kappa2 7.49460000 18001.0000
## 82 Subset12TRNEFG.alpha 0.33860000 17339.1346
## 83 Subset1HKYGX.mu 1.18330000 17737.5069
## 84 Subset2TRNEFG.mu 0.86810000 17265.7801
## 85 Subset3TRNEFG.mu 0.28770000 18001.0000
## 86 Subset4GTRIGX.mu 0.71030000 17282.8179
## 87 Subset5K80IG.mu 0.81110000 17571.3579
## 88 Subset6SYMG.mu 1.65750000 18001.0000
## 89 Subset7K80G.mu 1.93380000 18001.0000
## 90 Subset8K80G.mu 1.05690000 16988.5077
## 91 Subset9HKYGX.mu 0.59210000 18001.0000
## 92 Subset10TRNEFG.mu 1.18530000 17556.4438
## 93 Subset11JCG.mu 0.20370000 18001.0000
## 94 Subset12TRNEFG.mu 2.55030000 16378.8994
## 95 ucld.mean 0.00072704 197.9636
## 96 ucld.stdev 0.98360000 866.6601
## 97 meanRate 0.00068166 771.9061
## 98 coefficientOfVariation 1.29570000 143.9961
## 99 covariance 0.23880000 208.4930
## 100 Subset1HKYGX.treeLikelihood -1890.75370000 15350.8909
## 101 Subset2TRNEFG.treeLikelihood -2931.99210000 15300.1650
## 102 Subset3TRNEFG.treeLikelihood -2228.20130000 15363.0960
## 103 Subset4GTRIGX.treeLikelihood -4743.39680000 12599.6925
## 104 Subset5K80IG.treeLikelihood -5281.05330000 16367.2093
## 105 Subset6SYMG.treeLikelihood -5733.17950000 13937.6508
## 106 Subset7K80G.treeLikelihood -3536.68580000 11820.6143
## 107 Subset8K80G.treeLikelihood -8747.09380000 13462.9728
## 108 Subset9HKYGX.treeLikelihood -3646.62540000 13764.4572
## 109 Subset10TRNEFG.treeLikelihood -4254.89780000 12708.1486
## 110 Subset11JCG.treeLikelihood -949.69550000 8979.5379
## 111 Subset12TRNEFG.treeLikelihood -2630.68180000 15615.8354
## 112 speciation -581.46880000 546.6950Since four parameters had ESS values of less than 200 (tmrca(Archaeotetraodon), tmrca(Cretzeus), ucld.mean, coefficientOfVariation), a new run was started with identical settings and the chain length set to 250 million generations:
/home/linuxbrew/.linuxbrew/bin/beast -beagle_SSE -beagle_scaling dynamic concatchunks-ext2.xmlThe second run finished after 4.384 days.
The next steps were as follows:
Remove the first 10% of samples as burnin from both chains to produce truncated log files:
/home/linuxbrew/.linuxbrew/bin/logcombiner -burnin 50000001 concatchunks-ext.log concatchunks-ext-no-burnin.log /home/linuxbrew/.linuxbrew/bin/logcombiner -burnin 25000001 concatchunks-ext2.log concatchunks-ext2-no-burnin.logCombine the truncated log files:
/home/linuxbrew/.linuxbrew/bin/logcombiner -burnin 0 concatchunks-ext-no-burnin.log concatchunks-ext2-no-burnin.log concatchunks-ext-combined.logAnalyze the combined log file to assess ESS values:
/home/linuxbrew/.linuxbrew/bin/loganalyser -burnin 0 -ess concatchunks-ext-combined.log
Although the ESS values for the 4 previously problematic parameters all exceeded 200 when the two runs were combined, the ESS value of one more parameter (tmrca(Tarkus))unexpectedly dropped below the threshold:
## statistic median ESS
## 1 posterior -48684.61890000 4018.7952
## 2 prior -2109.34860000 1006.3031
## 3 likelihood -46575.17930000 21777.6112
## 4 treeModel.rootHeight 136.07150000 2642.6995
## 5 tmrca(Aipichthys) 122.59890000 2219.8782
## 6 tmrca(Archaeotetraodon) 36.68160000 300.9514
## 7 tmrca(Archaeus) 56.60900000 12713.2916
## 8 tmrca(Berybolcensis) 54.64290000 11218.4625
## 9 tmrca(Calatomus) 25.72650000 613.1224
## 10 tmrca(Chaetodontidae) 41.25430000 8190.6974
## 11 tmrca(Cretzeus) 93.90460000 237.4754
## 12 tmrca(Eastmanalepes) 52.64260000 22170.2330
## 13 tmrca(Eobothus) 57.91220000 2680.7794
## 14 tmrca(Eobuglossus) 43.31750000 7470.8790
## 15 tmrca(Eocoelopoma) 56.87570000 8276.2164
## 16 tmrca(Gasterorhamphosus) 75.05650000 3230.4774
## 17 tmrca(Homonotichthys) 99.22490000 2555.9326
## 18 tmrca(Hoplopteryx) 127.20650000 1478.2580
## 19 tmrca(Luvarus) 65.31620000 1396.4432
## 20 tmrca(Mahengechromis) 48.17900000 11840.4841
## 21 tmrca(Massamorichthys) 63.33570000 1009.8491
## 22 tmrca(Mene) 58.82670000 6847.9146
## 23 tmrca(Proacanthurus) 51.25330000 10184.5107
## 24 tmrca(Prosolenostomus) 59.06560000 1226.1886
## 25 tmrca(Rhamphexocoetus) 54.36880000 8583.6205
## 26 tmrca(Rhinocephalus) 56.60650000 3439.4406
## 27 tmrca(Root) 136.07150000 2642.6995
## 28 tmrca(Sphyraena) 59.76390000 12653.9557
## 29 tmrca(Tarkus) 49.98980000 126.1217
## 30 tmrca(Trachipterus) 25.29110000 1336.7531
## 31 tmrca(Triodon) 53.71510000 937.0322
## 32 tmrca(Turkmene) 64.05950000 1416.0132
## 33 tmrca(Zenopsis) 37.73790000 802.1615
## 34 birthDeath.meanGrowthRate 0.01850000 5668.8584
## 35 birthDeath.relativeDeathRate 0.04530000 23256.0832
## 36 Subset1HKYGX.kappa 3.30360000 26678.2412
## 37 Subset1HKYGX.frequencies1 0.38220000 27000.0000
## 38 Subset1HKYGX.frequencies2 0.23020000 27000.0000
## 39 Subset1HKYGX.frequencies3 0.15220000 27000.0000
## 40 Subset1HKYGX.frequencies4 0.23400000 27000.0000
## 41 Subset1HKYGX.alpha 0.98870000 26584.5037
## 42 Subset2TRNEFG.kappa1 4.82280000 27000.0000
## 43 Subset2TRNEFG.kappa2 1.81350000 26752.9895
## 44 Subset2TRNEFG.alpha 0.67290000 26917.0274
## 45 Subset3TRNEFG.kappa1 3.05000000 27000.0000
## 46 Subset3TRNEFG.kappa2 4.84750000 27000.0000
## 47 Subset3TRNEFG.alpha 0.46620000 27000.0000
## 48 Subset4GTRIGX.ac 0.17750000 27000.0000
## 49 Subset4GTRIGX.ag 0.41500000 25570.6224
## 50 Subset4GTRIGX.at 0.10570000 27000.0000
## 51 Subset4GTRIGX.cg 0.34230000 26434.8037
## 52 Subset4GTRIGX.gt 0.14860000 26976.0510
## 53 Subset4GTRIGX.frequencies1 0.30190000 26472.4252
## 54 Subset4GTRIGX.frequencies2 0.21200000 26641.8780
## 55 Subset4GTRIGX.frequencies3 0.31550000 26599.0962
## 56 Subset4GTRIGX.frequencies4 0.16930000 26366.7657
## 57 Subset4GTRIGX.alpha 0.44520000 27000.0000
## 58 Subset5K80IG.kappa 3.55900000 27000.0000
## 59 Subset5K80IG.alpha 0.31910000 26404.5070
## 60 Subset6SYMG.ac 0.31370000 25467.5781
## 61 Subset6SYMG.ag 1.60920000 27000.0000
## 62 Subset6SYMG.at 0.24730000 27000.0000
## 63 Subset6SYMG.cg 1.23790000 26676.0355
## 64 Subset6SYMG.gt 0.24420000 27000.0000
## 65 Subset6SYMG.alpha 0.52000000 26757.9001
## 66 Subset7K80G.kappa 2.93550000 27000.0000
## 67 Subset7K80G.alpha 0.70880000 27000.0000
## 68 Subset8K80G.kappa 3.17590000 27000.0000
## 69 Subset8K80G.alpha 1.22000000 26734.3603
## 70 Subset9HKYGX.kappa 3.59430000 26262.9651
## 71 Subset9HKYGX.frequencies1 0.20920000 26249.4586
## 72 Subset9HKYGX.frequencies2 0.25890000 26904.3496
## 73 Subset9HKYGX.frequencies3 0.30580000 26158.2443
## 74 Subset9HKYGX.frequencies4 0.22510000 26447.5814
## 75 Subset9HKYGX.alpha 0.62670000 26428.7697
## 76 Subset10TRNEFG.kappa1 4.17090000 26416.2617
## 77 Subset10TRNEFG.kappa2 6.01590000 27000.0000
## 78 Subset10TRNEFG.alpha 0.50510000 26076.5998
## 79 Subset11JCG.alpha 0.38700000 27000.0000
## 80 Subset12TRNEFG.kappa1 3.08690000 26675.3958
## 81 Subset12TRNEFG.kappa2 7.50120000 27000.0000
## 82 Subset12TRNEFG.alpha 0.33850000 27000.0000
## 83 Subset1HKYGX.mu 1.18280000 26828.6938
## 84 Subset2TRNEFG.mu 0.86800000 26255.6956
## 85 Subset3TRNEFG.mu 0.28790000 27000.0000
## 86 Subset4GTRIGX.mu 0.71010000 25129.2484
## 87 Subset5K80IG.mu 0.81080000 26461.5267
## 88 Subset6SYMG.mu 1.65730000 27000.0000
## 89 Subset7K80G.mu 1.93370000 26130.1157
## 90 Subset8K80G.mu 1.05650000 27000.0000
## 91 Subset9HKYGX.mu 0.59240000 25762.3514
## 92 Subset10TRNEFG.mu 1.18560000 26783.9906
## 93 Subset11JCG.mu 0.20370000 27000.0000
## 94 Subset12TRNEFG.mu 2.55020000 24805.2309
## 95 ucld.mean 0.00072680 294.7377
## 96 ucld.stdev 0.98350000 1554.3464
## 97 meanRate 0.00068161 995.7414
## 98 coefficientOfVariation 1.29670000 231.1065
## 99 covariance 0.24070000 453.1176
## 100 Subset1HKYGX.treeLikelihood -1890.75160000 22436.7525
## 101 Subset2TRNEFG.treeLikelihood -2932.00810000 23328.1981
## 102 Subset3TRNEFG.treeLikelihood -2228.22220000 23305.2617
## 103 Subset4GTRIGX.treeLikelihood -4743.39800000 18857.7749
## 104 Subset5K80IG.treeLikelihood -5281.08270000 22518.3527
## 105 Subset6SYMG.treeLikelihood -5733.24140000 21927.9543
## 106 Subset7K80G.treeLikelihood -3536.71940000 17624.6335
## 107 Subset8K80G.treeLikelihood -8746.96880000 16840.0116
## 108 Subset9HKYGX.treeLikelihood -3646.70940000 21631.2317
## 109 Subset10TRNEFG.treeLikelihood -4254.92840000 19433.2103
## 110 Subset11JCG.treeLikelihood -949.68370000 14102.6829
## 111 Subset12TRNEFG.treeLikelihood -2630.64210000 19591.9161
## 112 branchRates 0.00000000 25000.0000
## 113 speciation -581.49490000 733.9149To assess whether this reflects a more general problem with convergence, maximum clade credibility trees were constructed for both the first run alone (18,000 samples excluding burnin) and the combined run (27,000 samples excluding burnin) and compared to each other:
/home/linuxbrew/.linuxbrew/bin/treeannotator -burnin 50000000 -heights median concatchunks-ext.trees chunks-ext-run1.tre
/home/linuxbrew/.linuxbrew/bin/logcombiner -trees -burnin 50000001 concatchunks-ext.trees concatchunks-ext-no-burnin.trees
/home/linuxbrew/.linuxbrew/bin/logcombiner -trees -burnin 25000001 concatchunks-ext2.trees concatchunks-ext2-no-burnin.trees
/home/linuxbrew/.linuxbrew/bin/logcombiner -trees -burnin 0 concatchunks-ext-no-burnin.trees concatchunks-ext2-no-burnin.trees concatchunks-ext-combined.trees
/home/linuxbrew/.linuxbrew/bin/treeannotator -burnin 0 -heights median concatchunks-ext-combined.trees chunks-ext-combined.treAs can be seen, the ages are nearly identical:
BEAST under the extended calibration set: first run only vs. combined run
Comparison of the BEAST analyses under the uncorrelated lognormal clock and 12/28 calibrations with the manuscript tree
BEAST: Fixed local clocks
In the next step, a number of nodes were assigned their own (strict) clocks:
- Eupercaria (Canthigaster figueiredoi + Cephalopholis argus)
- Carangaria (Centropomus medius + Alepes kleinii)
- Ovalenteria (Xenentodon cancila + Emblemariopsis sp.)
- Pelagiaria (Chiasmodon niger + Psenes cyanophrys)
- Syngnathiformes (Syngnathus fuscus + Pseudupeneus maculatus)
- Gobiaria (Kurtus gulliveri + Periophthalmus barbarus)
- Lepophidium profundorum + Carapus bermudensis
- Rondeletiidae + Holocentridae (Rondeletia loricata + Myripristis leiognathus)
- Paracanthopterygii (Lampris guttatus + Gadus morhua)
The objective was to assign a separate clock to each of the six major percomorph subgroups (Eupercaria, Carangaria, Ovalentaria, Pelagiaria, Syngnathiformes, Gobiaria), and then to each of their sister groups that branched off successively closer to the root, ending with the Paracanthopterygii. However, as BEAST is unable to give local clocks to individual tips, only outgroups comprising two or more terminal taxa were assigned their own clocks, while the rates of Batrachoides and Anoplogaster were governed by a common tree-wide clock (set up by the clock.rate parameter) separate from all the local clocks. Both the local clocks as well as the tree-wide clock were strict, and all were assigned the same prior: a lognormal distribution with a mean of 0.005 (in real space), a standard deviation of 1, and an initial value of 0.005.
By default, BEAST assumes that if the fixed local clock model is selected, the subset of nodes that are to be assigned their own clocks is identical to the set of calibrated nodes. This was not the case in the analysis below. However, this problem can be solved in the following way: (1) In the “Taxa” tab of BEAUTi, enforce the monophyly of those nodes which are going to receive their own clocks but not the monophyly of the calibrated nodes. Note that this makes no difference in practice, since the analysis is to be run on a fixed topology, but it does prevent BEAST from assigning local clocks to the calibrated nodes. (2) In the “Priors” tab of BEAUTi, both the calibrated nodes and the “clock-bearing” nodes are expected to receive calibration priors. While these are only available for the former category, the latter category can be effectively freed from the need to specify priors by selecting the Use Tree Prior option in BEAUTi.
All the other settings (substitution models, calibration densities, the fixed topology operator mix) were identical to the uncorrelated lognormal clock analysis described above.
The analysis was run as follows:
/home/linuxbrew/.linuxbrew/bin/beast -beagle_SSE -beagle_scaling dynamic fixed-local-strict-13-calib.xmlThe chain reached the target length of 500 million generations after 11.626 days.
The effective samples sizes of individual parameters as calculated by LogAnalyser were as follows:
/home/linuxbrew/.linuxbrew/bin/loganalyser -burnin 50000000 -ess fixed-local-strict-13-calib.log## statistic median ESS
## 1 posterior -49123.84490000 2037.3359
## 2 prior -730.66560000 371.6547
## 3 likelihood -48392.96990000 11889.6246
## 4 treeModel.rootHeight 142.38790000 14889.8482
## 5 tmrca(Aipichthys) 141.47640000 16338.0549
## 6 tmrca(Berybolcensis) 73.45960000 13030.8789
## 7 tmrca(Calatomus) 33.12950000 864.5692
## 8 tmrca(Carangaria) 69.71650000 405.1534
## 9 tmrca(Carapus-Lepophidium) 60.37370000 16298.4431
## 10 tmrca(Chaetodontidae) 31.51700000 6679.4499
## 11 tmrca(Eastmanalepes) 49.54820000 12415.8822
## 12 tmrca(Eobuglossus) 50.93410000 2272.5621
## 13 tmrca(Eucoelopoma) 55.98260000 10615.3727
## 14 tmrca(Eupercaria) 71.27540000 142.9556
## 15 tmrca(Gobiaria) 57.76250000 616.5514
## 16 tmrca(Homonotichthys) 125.84800000 16307.8877
## 17 tmrca(Mcconichthys) 63.40650000 6719.6232
## 18 tmrca(Mene) 58.17530000 5287.7434
## 19 tmrca(Ovalentaria) 58.53990000 926.1633
## 20 tmrca(Pelagiaria) 73.88320000 954.1835
## 21 tmrca(Ramphexocoetus) 54.27350000 1819.7195
## 22 tmrca(Rondeletiidae-Holocentridae) 105.59100000 11628.5200
## 23 tmrca(Root) 142.38790000 14889.8482
## 24 tmrca(Syngnathiformes) 67.57600000 1080.1733
## 25 tmrca(Tarkus) 49.70150000 1083.8428
## 26 tmrca(Paracanthopterygii) 141.47640000 16338.0549
## 27 birthDeath.meanGrowthRate 0.01940000 7300.9057
## 28 birthDeath.relativeDeathRate 0.05580000 17849.9035
## 29 Subset1HKYGX.kappa 3.23410000 18001.0000
## 30 Subset1HKYGX.frequencies1 0.38410000 17771.2752
## 31 Subset1HKYGX.frequencies2 0.23270000 17483.9830
## 32 Subset1HKYGX.frequencies3 0.15110000 18001.0000
## 33 Subset1HKYGX.frequencies4 0.23090000 16701.7774
## 34 Subset1HKYGX.alpha 1.04300000 18001.0000
## 35 Subset2TRNEFG.kappa1 4.70270000 16211.6753
## 36 Subset2TRNEFG.kappa2 1.75900000 17135.2751
## 37 Subset2TRNEFG.alpha 0.68160000 18001.0000
## 38 Subset3TRNEFG.kappa1 3.02350000 17716.5092
## 39 Subset3TRNEFG.kappa2 4.82520000 16905.5261
## 40 Subset3TRNEFG.alpha 0.47330000 17606.3865
## 41 Subset4GTRIGX.ac 0.18730000 15377.2684
## 42 Subset4GTRIGX.ag 0.42460000 17598.3592
## 43 Subset4GTRIGX.at 0.10000000 16575.0251
## 44 Subset4GTRIGX.cg 0.36950000 18001.0000
## 45 Subset4GTRIGX.gt 0.14950000 18001.0000
## 46 Subset4GTRIGX.frequencies1 0.30510000 17825.4731
## 47 Subset4GTRIGX.frequencies2 0.20550000 16197.4361
## 48 Subset4GTRIGX.frequencies3 0.31080000 16847.2357
## 49 Subset4GTRIGX.frequencies4 0.17760000 18001.0000
## 50 Subset4GTRIGX.alpha 0.45330000 18001.0000
## 51 Subset5K80IG.kappa 3.52390000 18001.0000
## 52 Subset5K80IG.alpha 0.32360000 18001.0000
## 53 Subset6SYMG.ac 0.33140000 17418.7030
## 54 Subset6SYMG.ag 1.57810000 18001.0000
## 55 Subset6SYMG.at 0.24350000 18001.0000
## 56 Subset6SYMG.cg 1.25130000 17269.7941
## 57 Subset6SYMG.gt 0.26090000 18001.0000
## 58 Subset6SYMG.alpha 0.52430000 17327.8499
## 59 Subset7K80G.kappa 2.84290000 16893.2805
## 60 Subset7K80G.alpha 0.74240000 16946.4298
## 61 Subset8K80G.kappa 3.13520000 18001.0000
## 62 Subset8K80G.alpha 1.25100000 16472.9896
## 63 Subset9HKYGX.kappa 3.53970000 18001.0000
## 64 Subset9HKYGX.frequencies1 0.21280000 18001.0000
## 65 Subset9HKYGX.frequencies2 0.25950000 18001.0000
## 66 Subset9HKYGX.frequencies3 0.30070000 18001.0000
## 67 Subset9HKYGX.frequencies4 0.22630000 18001.0000
## 68 Subset9HKYGX.alpha 0.64480000 18001.0000
## 69 Subset10TRNEFG.kappa1 4.13090000 18001.0000
## 70 Subset10TRNEFG.kappa2 5.85360000 18001.0000
## 71 Subset10TRNEFG.alpha 0.52020000 17821.6012
## 72 Subset11JCG.alpha 0.38460000 18001.0000
## 73 Subset12TRNEFG.kappa1 3.02610000 18001.0000
## 74 Subset12TRNEFG.kappa2 6.89270000 18001.0000
## 75 Subset12TRNEFG.alpha 0.35850000 16840.6897
## 76 Subset1HKYGX.mu 1.14530000 16457.0492
## 77 Subset2TRNEFG.mu 0.87810000 18001.0000
## 78 Subset3TRNEFG.mu 0.29580000 18001.0000
## 79 Subset4GTRIGX.mu 0.73260000 18001.0000
## 80 Subset5K80IG.mu 0.82460000 18001.0000
## 81 Subset6SYMG.mu 1.69630000 18001.0000
## 82 Subset7K80G.mu 1.85100000 18001.0000
## 83 Subset8K80G.mu 1.06370000 18001.0000
## 84 Subset9HKYGX.mu 0.59430000 17868.0226
## 85 Subset10TRNEFG.mu 1.18480000 18001.0000
## 86 Subset11JCG.mu 0.21330000 18001.0000
## 87 Subset12TRNEFG.mu 2.42210000 18001.0000
## 88 clock.rate 0.00059583 6499.0917
## 89 Carangaria.rate 0.00038146 1707.5095
## 90 Carapus-Lepophidium.rate 0.00296050 17208.2276
## 91 Eupercaria.rate 0.00063338 250.5997
## 92 Gobiaria.rate 0.00177240 589.3144
## 93 Ovalentaria.rate 0.00051243 1231.6271
## 94 Pelagiaria.rate 0.00018009 2855.7223
## 95 Rondeletiidae-Holocentridae.rate 0.00016488 15539.6245
## 96 Syngnathiformes.rate 0.00146150 1436.4311
## 97 Paracanthopterygii.rate 0.00101200 16619.8225
## 98 meanRate 0.00068337 633.3408
## 99 coefficientOfVariation 0.62670000 633.9622
## 100 covariance 0.91110000 1054.6547
## 101 Subset1HKYGX.treeLikelihood -1961.68430000 11412.9578
## 102 Subset2TRNEFG.treeLikelihood -3050.01030000 15769.5321
## 103 Subset3TRNEFG.treeLikelihood -2312.82940000 8179.2027
## 104 Subset4GTRIGX.treeLikelihood -4909.92280000 12746.3787
## 105 Subset5K80IG.treeLikelihood -5428.25720000 10132.1210
## 106 Subset6SYMG.treeLikelihood -5933.73410000 10282.5876
## 107 Subset7K80G.treeLikelihood -3700.27180000 11552.6385
## 108 Subset8K80G.treeLikelihood -9197.00450000 12457.3059
## 109 Subset9HKYGX.treeLikelihood -3783.68050000 8926.9213
## 110 Subset10TRNEFG.treeLikelihood -4401.77360000 7695.6777
## 111 Subset11JCG.treeLikelihood -997.18140000 17236.3680
## 112 Subset12TRNEFG.treeLikelihood -2715.88600000 12128.8444
## 113 branchRates 0.00000000 25000.0000
## 114 speciation -575.32740000 320.6261Since the ESS values exceeded 200 for all parameters except one (tmrca(Eupercaria)), the maximum clade credibility tree with 95% HPD intervals for all node ages was constructed as a summary of the posterior distribution:
/home/linuxbrew/.linuxbrew/bin/treeannotator -burnin 50000000 -heights median fixed-local-strict-13-calib.trees fixed-local-strict-13-calib.treThis tree was compared to a preliminary tree based on ~253 million generations to see if the estimated node ages (and that of Eupercaria in particular) changed substantially:
Comparison between the preliminary and final fixed local clock trees (13 calibrations)
The 29-calibration version of the fixed local clock analysis was run using the following command, with all other settings identical to the 13-calibration analysis described above:
The analysis was run as follows:
/home/linuxbrew/.linuxbrew/bin/beast -beagle_SSE -beagle_scaling dynamic fixed-local-strict-29-calib.xmlThe chain stopped when it reached the specificed length after 12.212 days.
The LogAnalyser-generated summary is shown below:
/home/linuxbrew/.linuxbrew/bin/loganalyser -burnin 50000000 -ess fixed-local-strict-29-calib.log## statistic median ESS
## 1 posterior -49254.91010000 2434.0981
## 2 prior -802.45410000 343.2358
## 3 likelihood -48452.15530000 6045.9048
## 4 treeModel.rootHeight 142.59460000 17076.6986
## 5 tmrca(Aipichthys) 142.03190000 16781.5027
## 6 tmrca(Archaeotetraodon) 35.35890000 658.4703
## 7 tmrca(Archaeus) 55.07860000 14127.8210
## 8 tmrca(Berybolcensis) 71.52740000 13329.2519
## 9 tmrca(Calatomus) 37.72320000 940.5783
## 10 tmrca(Carangaria) 73.59020000 489.6684
## 11 tmrca(Carapus-Lepophidium) 59.88640000 14600.4244
## 12 tmrca(Chaetodontidae) 36.07980000 5150.9271
## 13 tmrca(Cretzeus) 126.86700000 16617.4014
## 14 tmrca(Eastmanalepes) 50.21910000 16540.2405
## 15 tmrca(Eobothus) 69.64970000 564.8522
## 16 tmrca(Eobuglossus) 54.53650000 1514.1785
## 17 tmrca(Eocoelopoma) 56.48070000 15252.7726
## 18 tmrca(Eupercaria) 77.52990000 186.9551
## 19 tmrca(Gasterorhamphosus) 72.36050000 1547.8148
## 20 tmrca(Gobiaria) 60.76200000 477.4315
## 21 tmrca(Homonotichthys) 127.35160000 17241.2467
## 22 tmrca(Hoplopteryx) 121.19720000 3847.2492
## 23 tmrca(Luvarus) 67.25050000 2017.7517
## 24 tmrca(Mahengechromis) 51.28850000 1832.4278
## 25 tmrca(Massamorichthys) 58.92270000 4292.1376
## 26 tmrca(Mene) 60.14150000 5179.8827
## 27 tmrca(Ovalentaria) 61.76030000 1363.4852
## 28 tmrca(Pelagiaria) 77.59100000 806.6438
## 29 tmrca(Proacanthurus) 49.37680000 8132.0320
## 30 tmrca(Prosolenostomus) 70.48130000 2028.6770
## 31 tmrca(Rhamphexocoetus) 56.62920000 2183.8936
## 32 tmrca(Rhinocephalus) 60.47450000 15964.6785
## 33 tmrca(Rondeletiidae-Holocentridae) 103.62580000 11596.3732
## 34 tmrca(Root) 142.59460000 17076.6986
## 35 tmrca(Sphyraena) 67.29320000 2291.1410
## 36 tmrca(Syngnathiformes) 72.36050000 1547.8148
## 37 tmrca(Tarkus) 52.32670000 567.8049
## 38 tmrca(Trachipterus) 22.14160000 17507.0383
## 39 tmrca(Triodon) 66.31820000 211.1453
## 40 tmrca(Turkmene) 54.92690000 7314.7810
## 41 tmrca(Zenopsis) 32.30250000 3187.7425
## 42 tmrca(Paracanthopterygii) 142.03190000 16781.5027
## 43 birthDeath.meanGrowthRate 0.01820000 7719.3617
## 44 birthDeath.relativeDeathRate 0.05020000 17687.3529
## 45 Subset1HKYGX.kappa 3.21390000 18001.0000
## 46 Subset1HKYGX.frequencies1 0.38520000 17217.6627
## 47 Subset1HKYGX.frequencies2 0.23190000 17965.1316
## 48 Subset1HKYGX.frequencies3 0.15100000 18001.0000
## 49 Subset1HKYGX.frequencies4 0.23040000 18001.0000
## 50 Subset1HKYGX.alpha 1.05020000 18001.0000
## 51 Subset2TRNEFG.kappa1 4.69450000 17363.5582
## 52 Subset2TRNEFG.kappa2 1.76780000 17993.2732
## 53 Subset2TRNEFG.alpha 0.68610000 17740.4138
## 54 Subset3TRNEFG.kappa1 3.02860000 18001.0000
## 55 Subset3TRNEFG.kappa2 4.82050000 15075.2321
## 56 Subset3TRNEFG.alpha 0.47340000 17723.5243
## 57 Subset4GTRIGX.ac 0.18880000 18001.0000
## 58 Subset4GTRIGX.ag 0.42650000 18001.0000
## 59 Subset4GTRIGX.at 0.10000000 17599.1877
## 60 Subset4GTRIGX.cg 0.37270000 17656.1057
## 61 Subset4GTRIGX.gt 0.14980000 18001.0000
## 62 Subset4GTRIGX.frequencies1 0.30550000 17970.4523
## 63 Subset4GTRIGX.frequencies2 0.20480000 18001.0000
## 64 Subset4GTRIGX.frequencies3 0.31060000 16904.8408
## 65 Subset4GTRIGX.frequencies4 0.17800000 17955.3471
## 66 Subset4GTRIGX.alpha 0.45400000 17373.4553
## 67 Subset5K80IG.kappa 3.52170000 17830.6014
## 68 Subset5K80IG.alpha 0.32450000 18001.0000
## 69 Subset6SYMG.ac 0.33480000 17984.7861
## 70 Subset6SYMG.ag 1.58200000 17645.0258
## 71 Subset6SYMG.at 0.24540000 18001.0000
## 72 Subset6SYMG.cg 1.24950000 17698.9665
## 73 Subset6SYMG.gt 0.26100000 18001.0000
## 74 Subset6SYMG.alpha 0.52600000 17529.0012
## 75 Subset7K80G.kappa 2.83670000 18001.0000
## 76 Subset7K80G.alpha 0.74340000 17117.0746
## 77 Subset8K80G.kappa 3.12880000 17246.0245
## 78 Subset8K80G.alpha 1.25610000 17487.3237
## 79 Subset9HKYGX.kappa 3.53310000 17957.8614
## 80 Subset9HKYGX.frequencies1 0.21290000 18001.0000
## 81 Subset9HKYGX.frequencies2 0.25980000 18001.0000
## 82 Subset9HKYGX.frequencies3 0.30090000 18001.0000
## 83 Subset9HKYGX.frequencies4 0.22560000 18001.0000
## 84 Subset9HKYGX.alpha 0.64580000 16997.3722
## 85 Subset10TRNEFG.kappa1 4.12010000 18001.0000
## 86 Subset10TRNEFG.kappa2 5.82070000 18001.0000
## 87 Subset10TRNEFG.alpha 0.52140000 18001.0000
## 88 Subset11JCG.alpha 0.38510000 18001.0000
## 89 Subset12TRNEFG.kappa1 3.00570000 17780.5387
## 90 Subset12TRNEFG.kappa2 6.86550000 17692.5054
## 91 Subset12TRNEFG.alpha 0.36060000 17033.8502
## 92 Subset1HKYGX.mu 1.14460000 18001.0000
## 93 Subset2TRNEFG.mu 0.87870000 17710.6125
## 94 Subset3TRNEFG.mu 0.29700000 18001.0000
## 95 Subset4GTRIGX.mu 0.73490000 17883.7564
## 96 Subset5K80IG.mu 0.82610000 18001.0000
## 97 Subset6SYMG.mu 1.69220000 18001.0000
## 98 Subset7K80G.mu 1.85250000 18001.0000
## 99 Subset8K80G.mu 1.06560000 17875.7495
## 100 Subset9HKYGX.mu 0.59680000 16795.9376
## 101 Subset10TRNEFG.mu 1.18470000 18001.0000
## 102 Subset11JCG.mu 0.21330000 17663.5206
## 103 Subset12TRNEFG.mu 2.40260000 18001.0000
## 104 clock.rate 0.00060673 6822.7059
## 105 Carangaria.rate 0.00036147 1579.6530
## 106 Carapus-Lepophidium.rate 0.00297940 18001.0000
## 107 Eupercaria.rate 0.00054166 416.9227
## 108 Gobiaria.rate 0.00167370 490.1731
## 109 Ovalentaria.rate 0.00047408 1929.6305
## 110 Pelagiaria.rate 0.00017121 3161.6316
## 111 Rondeletiidae-Holocentridae.rate 0.00016878 14973.1137
## 112 Syngnathiformes.rate 0.00134710 3713.7027
## 113 Paracanthopterygii.rate 0.00094029 16643.5391
## 114 meanRate 0.00063772 612.9078
## 115 coefficientOfVariation 0.63710000 590.3531
## 116 covariance 0.91730000 668.7762
## 117 Subset1HKYGX.treeLikelihood -1963.34490000 13177.2867
## 118 Subset2TRNEFG.treeLikelihood -3050.34800000 12504.2159
## 119 Subset3TRNEFG.treeLikelihood -2316.59780000 14307.2615
## 120 Subset4GTRIGX.treeLikelihood -4918.48890000 11326.9575
## 121 Subset5K80IG.treeLikelihood -5439.47450000 11217.2113
## 122 Subset6SYMG.treeLikelihood -5933.09870000 14748.4056
## 123 Subset7K80G.treeLikelihood -3702.09060000 13827.7245
## 124 Subset8K80G.treeLikelihood -9209.33330000 11425.4101
## 125 Subset9HKYGX.treeLikelihood -3789.74140000 14233.0811
## 126 Subset10TRNEFG.treeLikelihood -4410.91230000 10183.5781
## 127 Subset11JCG.treeLikelihood -999.78800000 17085.3841
## 128 Subset12TRNEFG.treeLikelihood -2717.90640000 13060.8015
## 129 branchRates 0.00000000 25000.0000
## 130 speciation -582.71500000 328.4556Similar to the 13-calibration analysis, the ESS values of the 29-calibration run also exceeded 200 for all parameters except tmrca(Eupercaria). A summary tree was therefore constructed and compared to a preliminary tree based on the first ~221 million generations:
/home/linuxbrew/.linuxbrew/bin/treeannotator -burnin 50000000 -heights median fixed-local-strict-29-calib.trees fixed-local-strict-29-calib.tre
Comparison between the preliminary and final fixed local clock trees (29 calibrations)
Comparison of the BEAST analyses under the fixed local clocks and 12/28 calibrations with the manuscript tree
MCMCTree analysis
In PAML, subsets of a concatenated alignment cannot be simply ignored as in BEAST but must be removed from the file, so that the sum of partition lengths equals the total number of nucleotides (PAML manual: p. 13). Moreover, the sites that make up a partition must be adjacent in the alignment. Therefore, the following information from the best_scheme file generated by PartitionFinder was used to generate a PAML-compatible alignment:
1 locus42, locus1
2 locus57, locus3, locus25, locus2
3 locus4
4 locus61, locus5, locus55, locus63, locus23, locus47
5 locus16, locus54, locus45, locus31, locus7, locus64, locus6
6 locus15, locus24, locus8, locus11, locus53, locus52, locus51, locus20, locus26
7 locus14, locus17, locus9, locus27, locus22, locus28
8 locus10, locus21, locus13
9 locus62, locus37, locus59, locus29, locus12, locus58, locus46, locus66, locus44, locus18
10 locus40, locus39, locus49, locus19, locus32, locus38
11 locus35, locus60, locus33, locus43, locus30
12 locus34
13 locus36, locus48, locus65
14 locus56, locus50, locus41Using bash, replace whitespaces between each taxon name and the corresponding sequence with line breaks:
xargs -n 1 < concat.phy > concat2.phyManually remove the first line indicating the number of taxa and sites.
Now, use the structure of the new file (with names and sequences in alternating rows) to reorganize the chunks:
concat <- read.table("/Users/David/Downloads/concat2.phy", stringsAsFactors = FALSE) # Random check, part 1: print "locus 42" (sites 2051--2100) of the first taxon: substring(concat[2,], 2051, 2100) for(i in seq(2, nrow(concat), by = 2)) { chunks <- substring(concat[i,], seq(1, 3250, 50), seq(50, 3250, 50)) chunks[66] <- substring(concat[i,], 3251, 3297) ch1 <- paste(chunks[42], chunks[1], sep = "") ch2 <- paste(chunks[57], chunks[3], chunks[25], chunks[2], sep = "") ch3 <- paste(chunks[61], chunks[5], chunks[55], chunks[63], chunks[23], chunks[47], sep = "") ch4 <- paste(chunks[16], chunks[54], chunks[45], chunks[31], chunks[7], chunks[64], chunks[6], sep = "") ch5 <- paste(chunks[15], chunks[24], chunks[8], chunks[11], chunks[53], chunks[52], chunks[51], chunks[20], chunks[26], sep = "") ch6 <- paste(chunks[14], chunks[17], chunks[9], chunks[27], chunks[22], chunks[28], sep = "") ch7 <- paste(chunks[10], chunks[21], chunks[13], sep = "") ch8 <- paste(chunks[62], chunks[37], chunks[59], chunks[29], chunks[12], chunks[58], chunks[46], chunks[66], chunks[44], chunks[18], sep = "") ch9 <- paste(chunks[40], chunks[39], chunks[49], chunks[19], chunks[32], chunks[38], sep = "") ch10 <- paste(chunks[35], chunks[60], chunks[33], chunks[43], chunks[30], sep = "") ch11 <- paste(chunks[36], chunks[48], chunks[65], sep = "") ch12 <- paste(chunks[56], chunks[50], chunks[41], sep = "") concat[i,] <- paste(ch1, ch2, ch3, ch4, ch5, ch6, ch7, ch8, ch9, ch10, ch11, ch12, sep = "") } # Make sure that the new sequence rows have the desired length: for(i in seq(2, nrow(concat), by = 2)) { print(nchar(concat[i,])) } # Random check, part 2: "locus 42" should now correspond to sites 1--50. Does it? substring(concat[2,], 1, 50) # Yes! Now print the new alignment into a table: write.table(concat, "/Users/David/Grive/Alfaro_Lab/Acanthomorpha/sate-gblocks-clean-min-90-taxa/Concat/pamlconcat.phy", quote = FALSE, row.names = FALSE, col.names = FALSE)Add the following two lines to the beginning of the file:
118 3197 G G 12 100 200 300 350 450 300 150 497 300 250 150 150
Unlike BEAST, PAML cannot assign a separate substitution model to each partition, but it is capable of unlinking substitution model parameters across partitions (Warnock et al. 2014: ESM p. 2). Since moderate substitution model overparameterization usually does not pose a problem to Bayesian phylogenetic analyses (Ronquist & Deans 2010), each partition was assigned its own GTR+\(\Gamma\) (“REV”) model. Note that the unlinking of substitution models necessitates the use of empirical (nhomo = 0) rather than estimated base frequencies. Relative rate, equilibrium frequency, and alpha parameters were unlinked across partitions, but branch lengths were kept linked (options Mgene = 4 and Malpha = 1). To facilitate cross-platform comparisons, the root calibration was set to 120.5 Ma (same as the mean of the root prior used in BEAST) in baseml to calculate substitution rates.
The substitution rate estimation finished up in 5:46:48 and yielded the following values:
| Partition | Rate (subst. per 100 million years) |
|---|---|
| Gene 1 | 0.126608 |
| Gene 2 | 0.087695 |
| Gene 3 | 0.033200 |
| Gene 4 | 0.072678 |
| Gene 5 | 0.072362 |
| Gene 6 | 0.158487 |
| Gene 7 | 0.188372 |
| Gene 8 | 0.107932 |
| Gene 9 | 0.060550 |
| Gene 10 | 0.111402 |
| Gene 11 | 0.026772 |
| Gene 12 | 0.184536 |
For MCMCTree, the G option cannot be used, and the partitions must be given as multiple alignments one after another in a single file (http://groups.google.com/forum/#!topic/pamlsoftware/cC7mOgZnNiY). Such a file was compiled as follows:
Run the following code:
concat <- read.table("/Users/David/Downloads/concat2.phy", stringsAsFactors = FALSE) alignments <- matrix(nrow = 12*(nrow(concat) + 2), ncol = 1) for(i in 1:(nrow(concat)/2)) { for(j in 0:11) { alignments[(2*i-1) + j*(nrow(concat) + 2), ] <- concat[(2*i-1),] } chunks <- substring(concat[2*i,], seq(1, 3250, 50), seq(50, 3250, 50)) chunks[66] <- substring(concat[2*i,], 3251, 3297) alignments[2*i,] <- paste(chunks[42], chunks[1], sep = "") alignments[2*i + 1*(nrow(concat) + 2),] <- paste(chunks[57], chunks[3], chunks[25], chunks[2], sep = "") alignments[2*i + 2*(nrow(concat) + 2),] <- paste(chunks[61], chunks[5], chunks[55], chunks[63], chunks[23], chunks[47], sep = "") alignments[2*i + 3*(nrow(concat) + 2),] <- paste(chunks[16], chunks[54], chunks[45], chunks[31], chunks[7], chunks[64], chunks[6], sep = "") alignments[2*i + 4*(nrow(concat) + 2),] <- paste(chunks[15], chunks[24], chunks[8], chunks[11], chunks[53], chunks[52], chunks[51], chunks[20], chunks[26], sep = "") alignments[2*i + 5*(nrow(concat) + 2),] <- paste(chunks[14], chunks[17], chunks[9], chunks[27], chunks[22], chunks[28], sep = "") alignments[2*i + 6*(nrow(concat) + 2),] <- paste(chunks[10], chunks[21], chunks[13], sep = "") alignments[2*i + 7*(nrow(concat) + 2),] <- paste(chunks[62], chunks[37], chunks[59], chunks[29], chunks[12], chunks[58], chunks[46], chunks[66], chunks[44], chunks[18], sep = "") alignments[2*i + 8*(nrow(concat) + 2),] <- paste(chunks[40], chunks[39], chunks[49], chunks[19], chunks[32], chunks[38], sep = "") alignments[2*i + 9*(nrow(concat) + 2),] <- paste(chunks[35], chunks[60], chunks[33], chunks[43], chunks[30], sep = "") alignments[2*i + 10*(nrow(concat) + 2),] <- paste(chunks[36], chunks[48], chunks[65], sep = "") alignments[2*i + 11*(nrow(concat) + 2),] <- paste(chunks[56], chunks[50], chunks[41], sep = "") } write.table(alignments, "/Users/David/Downloads/pamlconcatpartitioned.phy", quote = FALSE, row.names = FALSE, col.names = FALSE, na = "")Manually add the information about the number of taxa and sites in each partition.
To obtain a single estimate that could be used for the gamma-Dirichlet hyperprior on rates (rgene_gamma), a weighted average of these values was computed, with each rate weighted by the length of the corresponding partition:
## [1] 0.09550072The shape parameter of the gamma-Dirichlet distribution (\(\alpha\)) was set to 2 and the rate parameter (\(\beta\)) was chosen so that the mean (calculated as \(\frac{\alpha}{\beta}\)) was equal to the rate above (expressed as the number of substitutions per 10 million years):
## [1] 209.4225The hyperprior on the mean of the rate distribution is distributed as follows:
The mean of the gamma hyperprior on the variance of the log rate was set to 0.1 by setting \(\alpha\) equal to 1 and \(\beta\) equal to 10. This corresponds closely to the mean of the ucld.stdev hyperprior in BEAST (0.3 – note that while BEAST places the prior on the standard deviation of the rate distribution, MCMCTree assigns the prior to the variance, or the square of the standard deviation).
The lognormal distribution of rates is plotted below, with the mean and variance (in log-space) set equal to the means of the respective hyperpriors:
Finally, since there are multiple loci, the Dirichlet concentration parameter \(\alpha_D\) was specified and set to the default value of 1, which is described as producing “a reasonable partitioning” in the MCMCTree manual.
The full configuration file is shown below:
seed = -1
seqfile = pamlconcatpartitioned.phy
treefile = 12_cali_no_outgroups_corrected.tre
outfile = chunks.txt
ndata = 12 * Number of partitions
seqtype = 0 * Data type: nucleotides
usedata = 3 * Store the Hessian matrix for approximate likelihood computation in out.BV
clock = 2 * Uncorrelated lognormal relaxed clock
RootAge = 'B(9.8, 14.3, 1e-300, 0.05)' * P of less than 98 Ma = 10^(-300) and P of more than 143 Ma = 0.05
model = 7 * GTR
alpha = 0.1 * Following Alfaro et al.
ncatG = 8 * Following Alfaro et al.
cleandata = 0 * Do not remove sites with ambiguity
BDparas = 0.1 0.1 0.01 * Birth, death, sampling: following Alfaro et al.
kappa_gamma = 6 2 * No effect since usedata = 3
alpha_gamma = 1 1 * No effect since usedata = 3
rgene_gamma = 2 209.42 1 * Gamma-Dirichlet prior on mean rate: estimated using baseml under strict clock
sigma2_gamma = 1 10 1 * Gamma-Dirichlet prior on log rate variance
finetune = 1: .1 .1 .1 .1 .1 .1 * Auto finetune: times, musigma2, rates, mixing, paras, FossilErr
print = 2 * Print branch rates into an output file
burnin = 500000 * Following Alfaro et al.
sampfreq = 500 * Following Alfaro et al.
nsample = 15000 * Following Alfaro et al.The initial analyses (run under the usedata = 3 option to calculate the Hessian matrices for the branch lengths) produced the following warning messages:
xmax = 0.0000e+00 close to zero at 226!
xmax = 0.0000e+00 close to zero at 225!
xmax = 0.0000e+00 close to zero at 223!
xmax = 0.0000e+00 close to zero at 224!However, these did not cause baseml to crash, and the Hessian matrices were successfully written into out.BV files. Therefore, the usedata variable was set to 2, the out.BV files were moved to in.BV, and four separate MCMC chains with a length of 75 million generations (as specified in the configuration file above) were started.
Test for autocorrelation in PhyloBayes
Rationale: in order to perform Bayes factor model comparisons in PhyloBayes, it is necessary to first run the estbranches program from the Multidistribute package to calculate a variance-covariance matrix for branch lengths. As its input, estbranches requires a substitution model with fixed-value parameters (i.e., the parameters of the model are not estimated simultaneously with the branch lengths). A properly formatted model file can be generated from baseml output using one of the scripts in the package.
Compile
paml2modelinf.cfrom the Multidistribute package v 9/25/03:cc paml2modelinf.c arrayutl.c -lm -O -o paml2modelinfRun baseml on an unpartitioned version of the SortaDate alignment. This was done using a fixed tree topology with a single root calibration at 120.5 Ma (equal to the mean of the uniform distribution with support from 98 to 143 Ma) and the F84 substitution model, which is required by estbranches.
PhyloBayes uses Bayes factors with marginal likelihoods computed using thermodynamic integration (path sampling) for model comparisons. All comparisons are between the user’s model of choice and the default, unconstrained model, whose prior is defined for unrooted trees (Lepage et al. 2007). The commands below were used to perform comparisons between this model and (1) the autocorrelared lognormal model as well as (2) the uncorrelated gamma model, which is the closest equivalent of the uncorrelated models implemented in BEAST (the uncorrelated exponential model offered in BEAUTi is its special case).
Note that the .oes file used for model comparison has the following components:
- topology without tip names but with (what appears to be) branch lengths
- list of tip names and numbers
- list of parent-child triplets (with tips represented by their numbers)
- variance-covariance matrix
According to the manual, the variance-covariance matrix can be obtained using Thorne et al.’s estbranches. The PAML manual states that MCMCTree under the usedata = 3 setting performs the same function. Will the PAML-generated matrix be readable by PhyloBayes?
(Since PhyloBayes does not support partitioning, a new PAML analysis was run on an unpartitioned version of the dataset but with otherwise identical settings.)
../bf -cov chunkbrlen.oes -long -ln chunklnbf ../bf -cov chunkbrlen.oes -long -ugam chunkugambf
Uncorrelated gamma vs unconstrained: logBF = 36.6282 [36.4131 : 41.7097] Autocorrelated lognormal vs. unconstrained: logBF = 77.3395 [59.7214 : 78.3891]
Refs:
Lepage T, Bryant D, Philippe H, Lartillot N 2007 A general comparison of relaxed molecular clock models. Mol Biol Evol 24(12): 2669–80
Ronquist F, Deans AR 2010 Bayesian phylogenetics and its influence on insect systematics. Annu Rev Entomol 55: 189–206
Warnock RCM, Parham JF, Joyce WG, Lyson TR, Donoghue PCJ 2014 Calibration uncertainty in molecular dating analyses: there is no substitute for the prior evaluation of time priors. Proc R Soc B 282(1798): 20141013